It’s well known that a surprisingly small portion of a developer’s work week is dedicated coding. Big chunks of their time is dedicated to administrative tasks like refining user stories, managing epics, detailing acceptance criteria, and all the other work that happens around the coding. 

This administrative load reduces the time available for core development work, limiting how fast your team can move.

However, the same models that power AI coding assistants like Cursor, GitHub Copilot, and Windsurf AI are the same models you use in Claude or ChatGPT or Google Gemini. 

This means the AI coding assistants can be used for more than just writing code. 

By employing the right tooling and practices, AI can potentially cut down the time spent on writing, reviewing, and synchronising epics, stories, and acceptance criteria by a significant margin.

Leveraging AI for More Than Code

AI coding assistants can be used to reduce the administrative overhead that consumes developers’ time without requiring them to swap to a different app or a browser.

It can all be done directly within the IDE. Cursor and Windsurf AI allow developers to create “rules” – documents that instruct the AI on how to complete specific tasks. While they were intended to provide task-dependent context and guidance to the coding assistant, these rules can also be used to provide context and guidance in generating drafts and revisions of project and sprint documentation, user stories, and other essential agile artefacts.

Streamlining Workflows with MCP and Project Management Integration

The coding agents within these AI-powered IDEs can also be connected to popular project management tools like Jira and Linear through the Model Context Protocol (MCP).

MCP is an open standard designed to enable two-way communication between AI applications and external data sources or tools. This protocol allows AI assistants to pull information from these project management systems and even push updates like new tickets or status changes, further automating administrative tasks.

This integration means that an AI assistant, guided by predefined rules and connected via MCP, can:

• Draft a user story based on a brief description.
• Populate relevant fields in a Jira or Linear ticket.
• Update the status of tasks based on development progress.
• Flag inconsistencies or dependencies between different user stories or epics.

Example: Cursor Rule for Drafting a User Story

AI coding assistants like Cursor use rule files (for Cursor, .mdc files in a .cursor/rules directory, where .mdc is just Markdown with Cursor-specific metadata in the header) to guide the AI’s behaviour. These rules can define the AI’s persona, its understanding of project-specific conventions, and the desired output format for various tasks.

Here’s a very short, conceptual example of what a Cursor rule file for drafting a user story might look like:

---
description: "User Story Generation Rule"
globs:
alwaysApply: false 
---

You are an expert Agile Business Analyst. Your role is to help draft clear, concise, and actionable user stories.

### User Story Structure:

When asked to draft a user story, follow this format:

**As a** [type of user],
**I want to** [perform an action],
**So that** [I can achieve a goal/benefit].

### Clarification:

If the request to draft a user story does not include details about the user or action or benefit stop and ask for clarification on the user type, desired action, or intended benefit before drafting the story. Only the user can decide what the user story is about.

### Acceptance Criteria:

For each user story, also draft a preliminary list of acceptance criteria. Start with at least three criteria.

- Acceptance Criterion 1:
- Acceptance Criterion 2:
- Acceptance Criterion 3:

### Task Generation:

Suggest 2-3 initial development tasks that would be required to implement this user story.
- Task 1:
- Task 2:

### Final Step:

Follow the user's instructions for any requested changes. After each change ask the user if the User Story is complete. If they confirm it is complete, use the Jira MCP server to add the User Story to the current project.

This rules file instructs the AI on the standard user story format, the need for acceptance criteria and related tasks. It has a final step that instructs the AI to use a Jira tool to add the created user story to the current project.

It doesn’t make up the User Story itself. That thinking still needs to be done by the developer who understands the broader context of the project beyond the code. It does however, rely on the AI to generate initial acceptance criteria and tasks. How well these will match your developers’ intentions depends on how well represented your product is in the AI’s training data.

Now, this rules file is just a draft. It will need tweaks to work consistently in your codebase. Use it more as a source of inspiration. What other steps in your process can you automate or streamline using the AI in your team’s coding assistant? And don’t forget that you can use the AI coding assistant to write the rules files for you.

Remember: Your Coding Assistant is an Everything Assistant

For now, the AI under the hood of your coding assistant is a SOTA frontier model that can do more than just code. With the right rules files and attached to the right MCP servers, your coding assistant can do everything any other AI tool can do. All from the one interface. Make the most of it to accelerate your team.

So, you’re looking at building Large Language Model (LLM) agents. With the recent increases in model capability at all sizes, and the feature-rich frameworks available (like LangChain, LlamaIndex, AutoGen, CrewAI, etc.), getting agents up and running is easier than ever. 

However, there’s a significant jump from an initial functional prototype to a system that’s reliable, performs consistently well, and executes affordably when running in production. 

It turns out, building the agent is the easy part. Making it truly performant is where the real challenges lie.

This article dives into that gap. We’ll look at the common hurdles faced when taking LLM agents into production and explore the essential tools and capabilities needed to overcome them.

Let’s get into it.

Why Agent Performance is Hard

Transitioning an LLM agent from a proof-of-concept to production-ready means tracking and addressing reliability, consistency, cost, and overall quality. Here are some of the key challenges:

• Hallucinations & Accuracy: hallucination poses a significant risk, especially if your application needs high precision. Ensuring factual consistency is crucial but surprisingly hard to guarantee consistently.
• Latency & Scalability: Users expect reasonably fast responses. Agents, particularly those running complex reasoning chains or calling multiple external tools, need to deliver results within acceptable timeframes, even when usage ramps up. A prototype that works fine with a few test requests might be too slow under real production load, or scaling it might become prohibitively expensive.
• Cost Management: LLMs are accessed via APIs charging per token (input and output). These costs can vary based on model, context size, caching and inference provider. You need ways to track costs granularly – maybe per user, per feature, or per agent run – but this often requires specific tooling.
• Prompt Brittleness & Engineering: Getting prompts right is more art than science sometimes. Crafting prompts that consistently guide the LLM to the desired behaviour is an iterative and often difficult process. Keeping these prompts effective as models update or requirements shift adds another layer of maintenance overhead. 
• Context Window Limitations: LLMs can only “remember” a certain amount of information from the current interaction (their context window). In long conversations or complex tasks requiring information recall over many steps, they can forget earlier instructions or details, limiting their effectiveness. A long context window that might make an agent performant, will also make it more expensive to run.
• Complex Planning & Error Handling: Agents can struggle with tasks needing sophisticated multi-step planning. If an Agent can get each step right 90% of the time, if your process has 6 steps you’ll get a successful result only 60% of the time. 
• Data Quality & Bias: The agent’s output is heavily influenced by its training data and any data you feed it at runtime (e.g., via Retrieval-Augmented Generation – RAG). If that data is poorly structured, incomplete, or biased, you risk getting inaccurate results.
• Security & Privacy: Integrating LLM agents brings security considerations. Think about potential data leaks, prompt injection attacks (where users trick the agent with malicious input), and the need to handle sensitive user data securely and comply with regulations like GDPR.

These aren’t just theoretical concerns. Companies like Acxiom, for example, faced difficulties debugging complex multi-agent setups and found they needed platforms like LangSmith to get the visibility required for optimisation and cost management. Research from Anthropic also suggests that simpler, more composable agent designs often prove more successful than highly complex, monolithic ones, highlighting that managing complexity itself is a major challenge. In Acxiom’s case, they were working with 60 LLM calls and over 200k tokens of context to answer a client request. 

The challenges in building on top of LLMs are interconnected. Making a prompt more complex to improve accuracy will increase token count, cost, and latency. Simplifying logic to reduce latency might increase errors. Optimisation becomes a balancing act. This is where dedicated tooling becomes essential and you need to move beyond the basics of looking at logs and monitoring API call rates to true observability.

While monitoring tracks known metrics (errors, uptime), observability gives you the tools to understand why your system behaves the way it does, especially when things go wrong unexpectedly. Given the non-deterministic nature and potential for novel failure modes in LLMs, observability is critical for diagnosing issues that simple monitoring might miss.

Key Capabilities for Managing LLM Agents

Observability and evaluation platforms offer a suite of core capabilities designed to help you manage the performance, cost, and reliability of LLM agents.

Each platform has its own strengths and weaknesses, but they all offer variations on the same functionality:

• Monitoring & Tracing: This is foundational. It means capturing detailed records of everything your agent does – tracing the entire execution flow of a request, logging inputs, outputs, intermediate steps (like tool calls), latency for each step, cost, and any errors. Good platforms visualise these traces, making complex workflows easier to understand. For development teams, this detailed tracing is invaluable for debugging. You can quickly find latency bottlenecks, pinpoint where errors occurred in a chain, and see exactly how the agent produced a specific output, saving hours of diagnostic time. 
• Cost Tracking & Analysis: With token-based pricing being common, keeping costs under control is vital. These platforms monitor token usage (prompts and completions), calculate costs per API call or session, and let you slice and dice cost data by user, feature, model, etc. Dashboards help spot trends, identify cost spikes, and find optimisation opportunities (like do you try using cheaper models for certain tasks). 
• Experimentation & Evaluation: Improving agent performance requires systematic experimentation. Platforms facilitate this with features like A/B testing (comparing prompts, models, parameters side-by-side), automated quality metrics (like BLEU, ROUGE, BERTScore), workflows for collecting human feedback, using another LLM to evaluate outputs (“LLM-as-a-Judge”), and interactive “playgrounds” for quick prompt testing. These tools allow teams to take a data-driven approach, test hypotheses rigorously, validate changes before deploying them, measure performance against benchmarks, and continuously refine agent quality. 
• Dataset Management: Good evaluation needs good data. Platforms often include tools to create, manage, and version datasets specifically for evaluating agent performance or even fine-tuning. You might import existing data, generate synthetic data, or – very usefully – create evaluation datasets directly from problematic production traces you identified via monitoring. This lets you build targeted tests based on real-world failures, ensuring your fixes address actual issues. 
• Prompt Management (CMS): Prompts are central to agent behavior, but managing them can get messy. Many platforms now offer Prompt Management features, acting like a Content Management System (CMS) for your prompts. This typically includes a central registry, version control, collaboration features, and integrated testing. A key benefit for many teams is the ability to update and deploy prompt changes directly from the platform’s UI, without needing a full application code change or redeployment. This dramatically speeds up the iteration cycle for prompt engineering and can even allow non-technical domain experts to contribute to prompt refinement.

The real value comes from how these capabilities integrate. You might use monitoring to spot a pattern of poor responses, use those traces to create an evaluation dataset, test a new prompt against that dataset using experimentation tools, deploy the winning prompt via the prompt management UI, and then monitor its impact on performance and cost – all within the same platform. This integrated feedback loop is key for continuous improvement.

Of course, platforms vary. Some excel at deep tracing, others have prompt management UIs to allow non-developers to contribute, some come from a broader MLOps background with deep evaluation features, and others focus on simplicity and cost-effectiveness. This means you need to consider your specific needs when choosing.

5 Platforms For Agent Optimisation

Let’s look briefly at five platforms offering relevant observability and evaluation capabilities: Langsmith, Helicone, Weights & Biases (W&B) Weave, Langfuse, and PromptLayer.

Langsmith

Developed by the LangChain team, Langsmith integrates very tightly with the LangChain/LangGraph ecosystem. Its strengths are detailed tracing, debugging, evaluation, and monitoring, especially for complex chains built with LangChain (their core framework available in Python and JavaScript). 

It’s a solid choice if your team is heavily invested in LangChain. It offers debugging traces, monitoring dashboards, cost tracking (per trace), a testing/evaluation framework with UI configuration, dataset management (including creation from traces), and a “Prompt Hub” for UI-based prompt management and deployment. 

Integration is trivial for LangChain users. Pricing includes a free developer tier and paid plans ($39/user/mo Plus) suitable for small teams, with usage-based costs for extra traces.

Helicone

Helicone positions itself as an open-source observability platform focused on ease of use and cost management. Its standout features are super-simple integration (often just a one-line change via a proxy for your inference provider’s API), strong cost tracking (per user/model, caching), and flexibility (self-hosted or cloud). 

It’s great if you prioritise rapid setup, tight cost control, or open-source. It monitors core metrics (latency, usage, cost, TTFT), supports prompt experiments/evaluations (including LLM-as-a-judge via UI), dataset curation, and UI-based prompt editing, versioning and deployment. 

Integration via proxy is very fast; SDKs are also available. Pricing is attractive, with a generous free tier, a Pro plan ($20/seat/mo + add-ons for prompt/eval), and a cost-effective Team plan bundling features. The open-source self-hosting option offers maximum control.

Weights & Biases (W&B) Weave

Weave is the LLM component of the established W&B MLOps platform. It leverages W&B’s strengths in experiment tracking, model versioning, and dataset management, extending them to LLMs. It emphasises rigorous evaluation and reproducibility. Best suited for data science/ML teams, especially those already using W&B, needing sophisticated evaluation and MLOps integration. 

It offers tracing linked to experiments, cost tracking, a powerful evaluation framework (pipelines, scorers, RAG eval), robust dataset management integrated with evaluation, and SDK/API integrations. 

Pricing includes a limited free tier and a Pro plan ($50/mo+) with usage-based costs for data ingestion. 

Langfuse

Langfuse is another prominent open-source LLM engineering platform (often seen as a Langsmith alternative) offering tracing, analytics, prompt management, and evaluation. 

It appeals to teams wanting open-source flexibility, self-hosting, or broad framework support beyond LangChain. 

It provides deep tracing (visualised), session/user tracking, cost tracking, extensive evaluation features (datasets from traces, custom scoring, annotation queues), dataset management, and broad SDK/integration support (including OpenTelemetry). 

Its UI prompt management allows no-code deployment via labels (production/staging). Pricing is SME-friendly: a generous free cloud tier, affordable Core ($59/mo) and Pro cloud plans, and the FOSS self-hosting option.

PromptLayer

PromptLayer focuses heavily on the prompt engineering lifecycle: management, versioning, testing, collaboration, and observability, with a strong emphasis on visual tooling (no-code prompt editor, visual workflow builder). Ideal for teams needing rapid prompt iteration, cross-functional collaboration (engineers, PMs, content specialists), and visual development. 

It offers request logging, performance dashboards, cost tracking, prompt-centric experimentation (A/B testing, backtesting, human/AI grading), and SDK/API integrations. 

Its core strength is the “Prompt Registry” – a visual CMS allowing no-code prompt editing, versioning, and importantly, UI-driven deployment decoupled from code releases. Pricing includes a limited free tier and a Pro plan ($50/user/mo) with a high request limit.

Comparing The Platforms

Selecting the right platform involves weighing features, integration effort, cost, and how well it fits your team’s specific situation. Here are some key trade-offs you will want to consider:

• Open Source vs. Closed Source: Helicone and Langfuse offer open-source versions. This gives you potential cost savings via self-hosting, more data control, and avoids vendor lock-in. The trade-off is the technical overhead of setting up, maintaining, and scaling it yourself. Closed-source, managed cloud options (Langsmith, W&B Weave, PromptLayer) offer convenience and support but come with subscription fees and less control. You need to balance the desire for control/savings against operational resources. 
• Ease of Integration: How quickly can you get value? Helicone’s proxy approach is near-instant. Langsmith is very easy if you’re already using LangChain. SDK-based integrations (common to most) require more code but offer flexibility. OpenTelemetry support provides standardisation but involves its own setup. Consider your team’s skills and how fast you need to get observability in place. 
• Breadth vs. Depth: W&B Weave offers the broadest scope, embedding LLM observability within a full MLOps platform – powerful, but maybe overkill if you only need LLM tooling. PromptLayer goes deep on prompt management and visual workflows, perhaps with less emphasis on deep tracing than others. Langsmith and Langfuse offer a strong balance across tracing, evaluation, and prompt management. Helicone focuses tightly on core observability, cost, and ease of use. Identify your biggest pain point (debugging? prompt iteration speed? evaluation rigor? cost?) and align the platform’s focus accordingly. 
• Pricing Models: Understand the costs. Per-user models are predictable by team size but can get pricey. Usage-based components (traces, requests, events, data ingestion) need careful estimation – W&B Weave’s ingestion cost, in particular, can be hard to predict. Free tiers are great for trials but often have limitations (users, retention) making them unsuitable for full production. Model your expected usage to compare the total cost of ownership (TCO). 
• UI Prompt Management: The ability to manage and deploy prompts via a UI without code changes is a major accelerator for iteration and collaboration. Langfuse, PromptLayer, Helicone, and Langsmith provide this clearly. If rapid prompt iteration involving non-engineers is key, prioritise platforms strong in this area.

Ultimately, there’s no single “best” platform. The optimal choice depends heavily on your context: your main challenges, budget, team skills, existing tools (especially LangChain), and the strategic importance you place on features like open-source or UI-driven prompt deployment.

Get Your Agents Production-Ready

Developing and deploying LLM agents can feel like two different worlds: the initial build can feel straightforward, but achieving consistent, reliable, and cost-effective performance in production is a complex engineering challenge. 

But that challenge can be navigated with the right platform. Find the one that fits your needs, integrate it into your process, and you can start optimising your Agents today.

It has become clear to everyone in tech that AI coding assistants have reached the point where adoption is a necessity. Developers can use these assistants to boost productivity across their development process, to integrate unfamiliar services, and even to navigate complex services with deep feature sets like AWS and Google Cloud Compute.

How much of a productivity boost these code assistants will give you depends on your developers, how common your tech stack is, and how common your product feature set is.

Building a restaurant recommendation site using React? AI will scaffold and draft your frontend and backend code.

Implementing novel trading algorithms on top of your bespoke low-latency networking stack? AI will still boost your developers’ effectiveness. 

One team Cline highlights on its blog used its open-source agent to 5× their productivity, allowing them to tackle features at a speed a team their size shouldn’t be capable of reaching.

Cursor reports similar gains when developers lean on its Composer agent for multi-step refactors inside its VS Code-fork IDE, while Copilot’s new Agent Mode shows Microsoft isn’t going to be left behind in the feature parity race.

Choosing the right AI coding assistant your business should settle on isn’t straightforward. Your business priorities and requirements need to guide the decision. Beyond platform integration, model flexibility, and pricing, you need to weigh open-source versus closed platforms, whether you want per-seat or credit-pool billing, and how much administrative control you need (SSO, RBAC, usage analytics, fine-grained model policy). The market shifts weekly, so every feature in this roundup reflects the tools’ states as of April 2025.

The Five Assistants at a Glance

We’ll focus on GitHub Copilot, Cursor, Windsurf, Cline and Roo Code. All of these revolve around Microsoft Visual Studio Code. Copilot is built into it by Microsoft. Windsurf and Cursor are forks of VS Code, while Cline and Roo Code are VS Code extensions.

Administrative Management and Control

Except for the FOSS Roo Code, all the coding assistants are business and enterprise ready, with Cline offering such features in Q2 2025. 

Of course SSO is available, and on top of that they each provide different methods for managing seats and costs.

Naturally Microsoft – they live and breathe enterprise – lead the way with GitHub Copilot’s admin features.

• GitHub Copilot (Enterprise tier) leads on enterprise governance: a full admin dashboard, org-wide usage analytics, SAML SSO, centralized billing, and RBAC for both features and model families. Admins can disable overage billing or restrict certain models with a toggle.
• Cursor (Business / Enterprise) matches most of that stack—dashboard, SAML/OIDC SSO, spending caps—but exposes fewer model policies because users can also bring their own keys.
• Windsurf offers a dashboard and RBAC in its Teams/Enterprise editions, but model choice is admin-only and SSO is an add-on for smaller teams.
• Cline plans team dashboards, RBAC, and SSO in Q2 2025; today you manage users through normal VS Code tooling and rely on your LLM provider’s console for spend tracking.
• Roo Code skips formal admin features entirely; teams rely on its “Custom Modes” JSON files for lightweight per-workspace policy.

Understanding Pricing and Cost Management

Copilot’s predictable seat price suits companies that value budget certainty over raw flexibility. Cursor mixes the two models: 500 premium calls are bundled, after which the org decides whether requests throttle or start metered billing. Windsurf decouples usage entirely with credits—great for bursty workloads, but something finance teams must watch. Cline and Roo Code shift every dollar to your own LLM account (OpenAI, Anthropic, Google, Azure, or local via Ollama/LM Studio); no assistant invoice appears at all.

Spending safeguards differ too. Cursor’s dashboard lets admins set a hard USD cap, while Copilot limits you to on/off overage flags. Windsurf currently requires manual top-ups; Cline and Roo Code inherit whatever alerts your LLM vendor provides.

Technical Capabilities and Features

Copilot’s strength is breadth: IDEs, CLI, GitHub Mobile, and GitHub.com all surface the same models and repository-wide context. Cursor and Windsurf embed AI deeper into a VS Code-derived IDE—Cursor favoring code intelligence and Windsurf emphasizing its Cascade workflow engine that strings agents into repeatable “Flows.” Cline and Roo Code expose the richest automation (browser control, shell commands, diff checkpoints, MCPs) but leave reliability up to the quality of the LLM you plug in.

Integration and Extensibility Options

• Copilot Extensions now forms its own marketplace, and admins can pre-approve or ban specific extensions.
• Cursor inherits the entire VS Code extension ecosystem plus an MCP bridge, so your existing tooling usually “just works”. However, Microsoft is starting to block the VS Code extension marketplace, as it was never included in the Open Source version of VS Code that Cursor is a fork of. But there is an open source VS Code extension marketplace
• Windsurf also supports VS Code extensions and has native MCP support for tool chaining. Like Cursor, it is also vulnerable to Microsoft enforcing VS Code extension marketplace access.
• Cline and Roo Code treat MCP as a first-class primitive: agents can spin up custom tools on the fly, a powerful way to script internal services without waiting for the vendor. Note: As these are VS Code extensions, not VS Code forks, they are not currently affected by Microsoft policy changes. 

Open-source posture matters here. Cline’s Apache-licensed repository lets enterprises audit and fork the agent; Roo Code is a community-run fork of Cline that layers “Custom Modes” for per-task defaults (model, temperature, tool set). Copilot, Cursor, and Windsurf sit on closed back ends even though they reuse the VS Code OSS editor.

Real-World Implementation Examples

For that 10-developer team needing simple user management:

• All-in on GitHub? Copilot Business at $19 per seat ($190 mo) is the cleanest fit, with GitHub org admins handling SSO and spend tracking. Comes with unlimited calls to OpenAI’s GPT-4.1, one of the current top coding models, thanks to Microsoft self-hosting it on Azure.
• VS Code power users? Cursor Business costs $800 mo but bundles 5000 fast premium calls before throttling—often enough if your team leans on autocomplete more than chat agents. You will spend much more, much much more, if you lean into Agent mode to accelerate development.
• Automation-hungry? Windsurf’s $1 500 mo plus credit packs enables its Cascade flows to auto-triage test failures or run Netlify deploys, but finance must keep credit top-ups in check. 
• Privacy-critical org? Cline with self-hosted Llama 4 or DeepSeek behind your firewall costs nothing beyond GPU time; you’ll wait for Q2 2025 to get enterprise dashboards.
• Pro-Open Source and want full control? Roo Code’s flexibility (BYOK with SOTA models plus local model access) gives you full control over spending and workflows. 

Detailed Platform Comparisons

GitHub Copilot

Still the go-to for teams living in GitHub issues, pull requests, and Actions. Its new Copilot Extensions layer brings first-party hooks into CI pipelines and popular SaaS tools, all constrained by org-level policies. The Enterprise tier ($39 user/mo) unlocks codebase indexing and granular usage analytics, plus SAML SSO.

Cursor

A polished AI-native IDE forked from VS Code OSS. Composer mode plans multi-file edits, runs tests, and can slow-queue requests after the 500-call allowance to avoid surprise bills. Admins set per-org dollar caps and see who is burning through the tokens; users can override built-in models by pasting their own OpenAI, Anthropic, Google, Azure or AWS Bedrock keys.

Windsurf

Targets advanced automation. Cascade agents chain LLM calls, and “Flows” save those chains for reuse—think one-click bug-to-fix pipelines. Live Preview panes and Netlify deploy hooks help full-stack teams.

Cline

Open-source VS Code extension with Plan/Act modes, full file I/O, terminal, and browser tools. MCP integration means agents can pull logs, query databases, or hit internal and external APIs seamlessly. Everything runs on your BYOK keys (or local models), keeping code inside your network. Team features land later this year.

Roo Code

Community fork of Cline that adds “Custom Modes.” A mode bundles default prompts, temperature, and model choice, letting teams create presets like “Architect Mode” for design docs or “Debug Mode” for stack traces. No dashboards or billing—usage is whatever your LLM vendor meters.

Business Scenarios and Tool Selection

Depending on your business needs you’re going to want to look at specific tools first. All the tools are rushing towards feature parity, so the choice comes down to your priorities:

• Single-platform simplicity: Copilot for GitHub-centric orgs; budgeting by headcount.
• Flexibility without self-hosting: Cursor balances seat pricing with admin spending caps and user-supplied keys.
• Cutting-edge automation: Windsurf if you can actively manage Blazes and don’t mind rapid UI changes.
• Data-sovereign enterprises: Cline or Roo Code when self-hosting or local inference is mandatory.
• Experimental or heterogeneous teams: Mix tools. Main product devs on Copilot, research on Roo Code, security on Cline with private models.

Making the Right Choice for Your Business

Match the assistant to the workflows you already have, the governance you require, and the budget model you can stomach. Re-evaluate every quarter; model quality, pricing, and features shift fast. A structured pilot to see what works, clear cost controls, and incremental rollout is the standard path to onboarding AI coding assistants without disrupting your delivery cadence.

Looking to the Future of AI-Assisted Development

GitHub Copilot continues to deepen GitHub-native workflows, Cursor pushes the VS Code envelope, Windsurf experiments with agentic pipelines, and the open-source duo of Cline and Roo Code keeps customisation and data privacy on the table. Choose deliberately, test rigorously, and keep an eye on the market, because in six months, maybe even three, the “top five” might look different again.

Cyber security is only going to get tougher. That’s one of the “benefits” of the AI wave we’re in. But there are things you can do to reduce risk – thousands of things. 

But here’s a list of the quick wins you can implement that will bring you the biggest step changes to your risk profile – the 20% effort that will bring you 80% of the benefit. And most of them are set once or automated or only require periodic check-ups. 

1. Locking Down Access and Authentication

Unauthorized access is still a primary path attackers use to get inside. Strong authentication and tight access controls are your foundational defences.

Turn On Multi-Factor Authentication (MFA)

• What: Activate MFA on all important business accounts.
• Why: This directly stops attacks that rely on stolen or weak passwords (like phishing, credential stuffing, brute-force). Adding that second check makes it much harder for attackers to take over accounts, even if they have the password.
• How: Prioritise MFA for email (Microsoft 365, Google Workspace), banking, cloud platforms (AWS, Azure, GCP), VPNs, and all admin accounts. Use authenticator apps (Google Authenticator, Microsoft Authenticator, Okta Verify) over SMS whenever possible, as they’re generally more secure. Follow best practices outlined in the Australian Cyber Security Centre (ACSC)’s MFA guidance.
• Tools: Many platforms offer built-in MFA. For broader control, consider Identity and Access Management (IAM) solutions:
  • Okta
  • Microsoft Entra ID

Use Strong, Unique Passphrases & a Password Manager

• What: Require long, unique passphrases for every single account, and use a password manager to handle them.
• Why: Stops attackers from easily guessing weak passwords or using credentials stolen from one breach to access other unrelated accounts (credential stuffing).
• How: Set a minimum passphrase length of 14 characters; using 4+ random words is a good technique. Roll out a solid business password manager to generate, store, and fill strong credentials securely. Make sure the manager itself is locked down with a strong master passphrase and MFA. Train your team on using it properly. Check the ACSC’s advice on passphrases.
• Tools: Look at well-regarded password managers with strong security:
  • 1Password
  • Bitwarden

Apply the Principle of Least Privilege (Limit Admin Rights)

• What: Tightly control and minimise who gets administrator privileges.
• Why: Radically reduces the damage if an account gets compromised. If a standard user account is breached, the attacker’s reach is limited, preventing system-wide damage compared to when an admin account is hit. It also makes privilege escalation attacks harder.
• How: Give users only the permissions they absolutely need for their job. Use separate, dedicated accounts for admin tasks – never use an admin account for everyday things like email or Browse. Review admin rights regularly. This lines up with the ‘Restrict Administrative Privileges’ strategy in the ACSC’s Essential Eight.

Manage Staff Access Changes Swiftly

• What: Have solid processes to immediately remove access for people leaving the company or changing roles.
• Why: Stops potential data theft or system misuse by former employees (an insider risk) and cleans up unused accounts that pose a lingering security risk.
• How: Make sure your HR off-boarding and role-change processes directly trigger IT access removal. Disable accounts and revoke permissions right away. Run regular checks on active user accounts to find and disable any orphaned or unnecessary ones.
  

2. Setting Up Robust Data Protection

Protecting your business and customer data is vital for keeping the lights on, meeting legal duties, and holding onto your reputation.

Set Up Regular, Automated Data Backups

• What: Run frequent, automated backups of all data that’s critical to your business.
• Why: Gives you a way to recover data and get back to business after major disruptions like ransomware, hardware meltdowns, accidental deletions, or even fires and floods.
• How: Pinpoint your critical data (financials, customer lists, operational files, system configs, cloud data like M365 backups – check provider options). Set up automatic daily backups using dependable backup software or services. This is a key part of the ACSC Essential Eight’s ‘Regular Backups’ strategy.
• Tools: Look into business-grade backup solutions:
  • Veeam
  • Acronis Cyber Protect

Keep Backups Separate and Test Your Restores

• What: Store backup copies somewhere separate (physically or logically) from your live data, and regularly test that you can actually restore from them.
• Why: Stops your backups from being wiped out by the same attack (especially ransomware) that hits your main systems. Untested backups might fail you when you need them most.
• How: Follow the 3-2-1 rule: at least 3 copies of data, on 2 different media types, with 1 copy offsite. For extra ransomware defence, make sure one copy is offline (air-gapped) or immutable (can’t be changed – via cloud storage with products like AWS S3 with Object Lock, Azure Blob Storage, and Google Cloud Storage with Bucket Lock). Schedule regular restore tests (maybe quarterly) to prove the backups work and your team knows the drill.

Secure Customer Information (Privacy Act Compliance)

• What: Know and meet your obligations for handling personal information under the Australian Privacy Act 1988.
• Why: Helps prevent costly data breaches involving customer info, keeps you compliant with the law, protects your reputation, and avoids fines.
• How: Only collect the personal data you actually need. Store it securely using good technical controls (encryption, access controls). Use secure methods for any transactions. Have a clear, easy-to-find privacy policy. Check guidance from the OAIC. 

3. Keeping Systems Healthy and Networks Secure

Updated, well-configured systems and networks are fundamental defences.

Patch Software and Operating Systems Quickly

• What: Regularly apply security patches and updates for all your software and operating systems.
• Why: Closes security holes that attackers know about and actively try to exploit. Patching quickly shuts these doors before attackers get in.
• How: Turn on automatic updates where it makes sense. Jump on patches for known exploited vulnerabilities fast, especially for systems facing the internet (aim for under 48 hours). Get other critical patches done promptly (e.g., within weeks). Get rid of any software or OS versions that are no longer supported and don’t get security updates. This directly supports the ACSC Essential Eight’s ‘Patch Applications’ and ‘Patch Operating Systems’ strategies.

Use and Maintain Endpoint Security Software

• What: Install, configure, and keep up-to-date endpoint security software (like Endpoint Detection and Response (EDR) and Endpoint Protection Platform (EPP) including antivirus/anti-malware) on all computers, servers, and work mobile devices.
• Why: Provides defence against malware like viruses, trojans, ransomware, and spyware by spotting and blocking known threats. Modern EDR tools also help catch new threats and give you tools to respond.
• How: Use solid built-in tools (like Microsoft Defender for Endpoint) or pick trusted third-party options. Make sure the software is always on, updating its threat intelligence constantly, and configured correctly.
• Tools: Top players in endpoint security include:
  • CrowdStrike Falcon
  • SentinelOne Singularity

Secure Your Office Wi-Fi and Router

• What: Lock down the basic security settings on your office wireless network and internet router.
• Why: Stops outsiders from jumping onto your network, listening in on wireless traffic, or using your internet connection for shady activities.
• How: Change the router’s default admin password immediately. Use strong Wi-Fi encryption (WPA2 minimum, WPA3 preferred) with a strong passphrase. If you offer guest Wi-Fi, put it on a separate, isolated network. Check ACSC advice for network security. 

4. Managing Cloud and Third-Party Risks

Using external services means managing the security risks that come with them.

Use MFA and Secure Configurations for Cloud Services

• What: Mandate MFA for all cloud service access (SaaS, PaaS, IaaS) and make sure you understand and apply basic secure setup within those platforms.
• Why: Protects your cloud accounts and data from being accessed by attackers with stolen credentials. Also tackles the common problem of data exposure due to simple cloud misconfigurations.
• How: MFA for cloud access isn’t optional. Understand the Shared Responsibility Model for each service – the provider secures the base infrastructure, you secure your data, users, and how you configure things inside the service. Apply essential settings like tight access controls, correct permissions, and logging within your cloud platforms. Use the ACSC’s cloud security guidance.

Be Smart About Third-Party Providers

• What: Do some basic security checks on your key suppliers, especially if they handle your sensitive data or provide critical IT functions (like IT support, payroll, SaaS tools).
• Why: Reduces your supply chain risk – where a security slip-up at your vendor becomes your problem, potentially leading to a breach impacting you.
• How: Ask potential vendors about their security practices before you sign up. Make sure contracts clearly spell out security duties, how data will be handled, and what happens if they have a breach. 

5. Empowering Your People and Preparing for Incidents

Much of your tech defences can be automated, but your team and your preparedness plan are a big part of your business’s security resilience.

Train Your Staff on Security Fundamentals

• What: Run regular, practical security awareness training for everyone.
• Why: Cuts down on security incidents caused by human mistakes, like clicking phishing links, falling for Business Email Compromise (BEC) scams, or accidentally installing malware.
• How: Focus training on real-world skills: spotting suspicious emails/links, password safety, safe Browse, and recognizing common scams. Keep it short, regular, and interesting. Use resources available from the ACSC. Build a culture where people feel safe reporting anything suspicious.
• Tools: Consider security awareness training platforms:
  • KnowBe4
  • Proofpoint Security Awareness Training

Have a Simple Incident Response Plan

• What: Create a clear, straightforward plan for what to do if you suspect a cyber incident.
• Why: Helps you react faster, stay calmer, and respond more effectively during a crisis, which minimises the damage, downtime, and cost. Being prepared prevents panic and leads to better decisions.
• How: Your plan should list: key internal contacts (IT, management), first steps for containing the problem (like isolating machines if possible), where backups are and how to restore them, and essential external contacts (cyber insurance, legal, ACSC hotline: 1300 292 371). Keep a printed copy somewhere safe – your network might be down when you need it.

Set Up Clear Incident Reporting Channels

• What: Make sure every employee knows who to tell internally if they see something suspicious or think an incident might be happening.
• Why: Allows for quick internal action to stop a potential threat. Also ensures serious incidents get reported externally to the right places (like the ACSC via ReportCyber) when required, helping everyone by improving threat intelligence and potentially involving law enforcement.
• How: Define a clear internal reporting point (e.g., their manager, IT helpdesk, security contact). Make sure relevant people know about the ACSC’s ReportCyber portal for reporting incidents affecting the business. 

Lock those cyber doors

By implementing these security measures, your business establishes interlocking defences against common cyber threats. This protects your operations, your data, and your reputation. 

The list is pretty much in order of priority. We’d recommend starting on 1 and 2 today, then keep working your way down through every item. Once everything is in place security will become second nature to your team.

All you have is $50,000 in seed funding, a vision for a niche marketplace product, and six months to turn that into something users will pay for. You’re going to keep it simple and aim for the widest availability: web-based with a mobile-first design. 

By standard development metrics, where projects need teams of 5-8 people, this combination of budget and timeline looks unworkable.

You need to build and launch a market-ready product with what typically covers just the design phase of a development project. This is a constant in business – you can’t afford and can’t afford to wait for the perfect team and the perfect circumstances.

To achieve this “vision” you’ll need to rethink how you approach product development. The standard process of separate design, development and testing phases won’t work when your runway looks like it is measured in weeks instead of months.

This isn’t about coding or features. It’s about finding what users will pay for and building it fast. You have to make every development hour count towards getting a product that can start making money.

Four Key Development Strategies For Success

Here’s how to build a market-ready product on a tight budget. It comes down to combining four approaches that speed up getting to revenue.

The first approach uses Lean Startup to focus on learning what works and cutting anything that doesn’t move you towards revenue. The second replaces the basic MVP with a Minimum Marketable Product (MMP) – something users will pay for from day one. The third uses a small nearshore team of 1-3 developers who can work through Lean cycles quickly while keeping costs manageable. The fourth uses AI tools for coding, design and testing to multiply what that small team can achieve.

These four elements let you launch a product that makes money while you learn what your market wants.

Maximising Development Efficiency Under Constraints

Here’s how those four components work together to get you to a product users will pay for. The first piece is building a Minimum Marketable Product (MMP) – something simple that delivers value from day one. Think of a subscription management app that just handles payments and user access. No bells, no whistles.

The second and third pieces work hand in hand. You need a small nearshore team of 1-3 developers.. One developer handles the UI/UX and testing while the others work across the full stack. And they use AI coding assistants like Cursor, GitHub Copilot and Windsurf to speed up their work – code completion to write faster, AI-generated tests to catch problems, and specialised UI and UX tools like Uizard to draft initial designs.

The fourth piece, Lean-focused Agile development, ties it all together with two-week development cycles. Build something, see how users respond, adjust based on that response. Then do it again. Each cycle moves you closer to what your market wants.

Budget Planning And Team Structure

Let’s talk about how that $50,000 gets spent across six months. Most of it goes to your development team – they build your product. The cost of a nearshore team depends on where they’re based, their skill level, and how many people you need. It also depends on what you’re building and what technology you’re using to build it.

You need to understand these costs to work out how many developers you can afford and for how long. 

Implementation Timeline And Technical Setup

Here’s the basic timeline for turning that $50,000 into something users will pay for. 

The first two weeks are about getting the technical foundations right. You can use AI to assist with this. There are different techniques and strategies still being worked on as these tools continue to evolve.

You and your team define what features go into the MMP and map out how users will move through the product. They build the UI designs for the parts of your product that will generate revenue.

As part of that process, they set up the software architecture and get the version control and project management tools running. If you’re using AI tools to speed up development, this is when they get set up and tested. By the end of week 2, you have a technical plan and everything in place to start building features.

Measuring Product Market Fit And Performance

So now your team is heads down building. The goal is to get a product you can launch and users can start interacting with and responding to as quickly as possible.

Once you launch, you need to stop building and start measuring what your users do. Your product will only work if users pay for it, so you need to know what they’re doing and what they think about what you’ve built. This means tracking four metrics that tell you if users find value in your product.

• Daily/Weekly/Monthly Active Users – shows you if users are coming back
• Customer acquisition cost – shows the efficiency of your marketing and sales efforts
• Revenue per user – indicates how effectively you’re monetising your product
• Churn rate – reveals whether you’re delivering enough value to retain users

By tracking these metrics you get a concrete picture of your product’s performance. And naturally you’ve instrumented your app so you know what features are being used and what features aren’t. 

This is also the time you reach out to users and get direct feedback on their experience with your app. 

Combining these information sources shows you where to focus your development budget on changes that move you towards revenue.

Managing Development Risks

Using a lean approach with a small team keeps costs down but comes with three risks you need to manage. 

The first is technical debt. Set up a simple system to log when you take shortcuts to hit deadlines, then schedule time to clean up that code. 

The second is budget monitoring. Your $50,000 can disappear fast when developers hit complex problems or need extra time to learn new tools, so check spending weekly. 

The third is scope creep. If you’re running solo, then it’s all about being very judicious about implementing any features that users request. If you have stakeholders, once they see the product taking shape and getting a foothold among users, they’ll have features of their own they’ll want to see implemented, and they will be harder to turn down than users.

Keep a list of requested features but stay focused on building what users will actually pay for.

The nearshore team benefits

Using a small, nearshore team supported by AI dev tools lets you build a product faster and cheaper than traditional development approaches. You get access to a bigger talent pool while keeping costs under control and you can adjust your team size as needed. 

The model works because you’re working with established providers with experienced developers. As more and more businesses move towards core teams augmented with team extensions, the developers at the extended team providers are the ones building up experience across multiple projects and technologies.

Making It Happen With What You’ve Got

Building a market-ready product with limited resources isn’t about having the perfect tech stack or a big team – it’s about ruthlessly focusing on what generates revenue. By combining Lean principles, a revenue-focused MMP and a small nearshore team supported by AI development tools, you can turn $50,000 and six months into a product users will pay for. This approach trades the comfort of large teams and long timelines for the reality of getting to market quickly with something that can sustain itself through revenue.

If you’re sitting on a product idea but don’t have the typical development budget, don’t let that stop you. Start by defining your MMP – what’s the simplest thing users will pay for? Talk to a few potential users. Confirm they’re willing to pay.

Then come and have a chat with us. Our passion is helping businesses punch above their weight in software product development. We’re always happy to talk about the strategies and costs of building software, so get in touch.

Your business runs on AWS with redundancy and failover that works 24/7 across availability zones. But cloud infrastructure doesn’t help when your team can’t access their workspace. A flood, fire, or local power outage stops work just as effectively as a cloud outage. Even something basic like a burst pipe can shut down operations.

All the multi-region cloud resilience in the world won’t help you if your team can’t work.

While your distributed infrastructure handles outages across data centers, your business still depends on a building. Teams work from desks, meet in conference rooms, and use on-premises resources. Moving to the cloud didn’t change these office-based workflows.

This mismatch between cloud infrastructure and office-bound operations creates a gap in your business continuity.

We’re going to cover the basics of closing that gap in this article.

Conducting An Office Dependency Audit

The first step in business continuity is doing an Office Dependency Audit. Map out what your team uses in the office that they can’t work without. Look at workstations, security tokens, shared hardware, and any planning that happens on whiteboards. Check for development environments that need specific hardware setups.

Are your operations reliant on hard copy documentation? Some departments love their shelf of SOP binders, and some staff members need everything printed out before they can work. These need to be reconsidered as part of continuity planning.

The audit will show you what needs backup solutions and which processes you’ll need to change so your team can work remotely. It’s the first step in building a plan that keeps your team working when they can’t get to their desks.

Building A Secure Remote Access Strategy

Once you’ve mapped your office dependencies, set up remote access that works for your team. This means going beyond a basic VPN to building a system that controls who connects to what and when they connect. Set up a central point where your IT team manages connections to your cloud services, AWS consoles and third-party platforms.

Zero Trust Network Access forms the base of this setup. Zero Trust means no user or device gets automatic access, no matter where they are connecting from. Each connection request needs verification, and the system tracks who is trying to connect and what they’re trying to do. This lets your team work from anywhere while keeping your systems and data secure.

AWS access is a key part of remote work planning for SaaS businesses. AWS Single Sign-On (SSO) handles the basics when your team works with multiple AWS accounts. Your technical team will use the AWS Command Line Interface (CLI) to do their work. SSO removes the security risk of storing access keys on personal machines.

The setup process is basic – install the AWS CLI, set up SSO, and you have one place to control who accesses what. Add mobile Multi-Factor Authentication (MFA) to SSO login and you get security that works whether your team is in the office or not.

SSO and MFA handle most access control needs, but you need backup options. Set up a central credential vault for systems that manage infrastructure or handle money. The vault gives your IT team a place to store and track credentials, and it can reset them after each use.

Set up the vault with its own authentication path using different providers than your SSO. This means if SSO goes down, or there’s a problem with your MFA provider, you can still access what you need.

Remote access solutions need to handle data protection requirements set by Australian regulators. This becomes important when your team works from home or temporary locations and accesses data that needs protection. The first step in meeting these requirements is listing out what data and systems your operations use.

Start by documenting your hardware, software, cloud services and external dependencies. List your team’s devices – both company-owned and BYOD – and map out your backup systems. This gives you a clear view of what needs protecting.

This documentation helps you meet regulatory requirements while making your risk management more effective. 

Managing Knowledge And Documentation

Your team needs to access operational knowledge when they can’t get to the office. A cloud-based documentation platform – a wiki or intranet – puts your processes, configurations and emergency procedures in one place. Your team can look up the steps they need to do their jobs from anywhere – which they can’t do with a shelf of SOP binders.

Keeping your documentation in the cloud means your team follows the same steps whether they’re in the office or working remotely. They don’t need to remember complex processes or rely on printouts when dealing with incidents.

Adapting Team Communication And Workflows

Your team needs a plan for working together when they can’t access the office. Set up communication through your collaboration platforms like Slack or Microsoft Teams. These are the tools your team uses every day, so they’ll keep using them during disruptions.

Set up backup communication methods in the case of extreme events. An SMS alert system or a calling tree for each team means you can reach everyone if your main platforms like Slack or Teams or even email go down. 

Put together message templates for building problems like evacuations and lockouts. This saves you writing important messages while your team is spread out. 

Investigate your phone provider’s business continuity features and make sure you have both staff members and documentation responsible for securely implementing all necessary redirects.

Your team’s daily routines that rely on physical presence need updating. For example, stand-up meetings that use physical boards or screens can move to Slack channels where team members post updates when they log in. This keeps project visibility while letting people work from different locations.

Support handovers also work differently when remote. Replace desk-side conversations and impromptu meetings with defined processes. Use short video calls for shift changes, write complete ticket updates, and document how to escalate issues when your team works from multiple locations.

Testing Your Business Continuity Plan

Your team needs to validate your continuity plan through testing. These can run from tabletop tests, where you talk through your planned processes and examine them from multiple perspectives to look for omissions, to office lockout drills where everyone works from home, testing AWS console access, 2FA procedures, remote stand-ups and support handovers. These drills show you where your remote setup needs work.

Drills uncover practical problems that planning meetings miss – like finding out the person with admin access needs a hardware key from their desk, or learning your backup communication system needs an app no one has installed. These basic issues determine whether an office lockout becomes a short disruption or stops your business from operating.

Your documentation process should give you insights into how prepared you are for a real event. Based on this you are going to need to decide on when and how often you will run drills. Do you run them monthly until all the parts are working smoothly? Or do you feel the risk is low enough you can run quarterly drills and spread out process adoption into longer timeframes? You will want to pay attention to staff turn-over and have a threshold where a drill is run so new staff members can participate and learn from it.

Putting it All Together

While your business has invested heavily in cloud infrastructure resilience, your physical office remains a single point of failure that could disrupt your operations. By mapping your office dependencies, establishing robust remote access protocols, centralising documentation, adapting team workflows, and regularly testing your plan through deliberate drills, you create a practical safety net that will keep your business running when the office becomes inaccessible.

Take a moment now to schedule that first office lockout drill. Pick a date in the next month, put it in your calendar, and start working through the office dependency audit. The time you invest now in preparing for workspace inaccessibility will pay dividends when your team needs to suddenly shift to remote operations – keeping your services running and your customers happy while your competitors scramble to adapt.

Teams adopting Agile development see measurable results. The changes show up in the numbers – delivery speed increases, cycle times drop by weeks, and customer satisfaction scores go up. These results come from how Agile works: breaking work into small pieces, changing direction when needed, and getting features to users quickly. The data backs this up – according to industry research, Agile projects succeed 64% of the time, making them about 1.5x more successful than traditional waterfall methods.

The results you’re seeing with Agile development reveal problems that extend beyond software teams. Studies show that 60-80% of project failures come from requirements, analysis, and change management issues. These problems are not just faced by developers. They show up across all departments.

Look at your marketing team missing deadlines when priorities shift. Watch sales teams struggle to track their pipeline. See HR work through long recruitment cycles. Your development team has already solved similar problems with Agile, which means you can help other departments use Agile to solve them too.

Building A Business Case For Agile Transformation

Build your case for Agile adoption using the data from your development team. The numbers tell the story – show other departments the 30% increase in delivery speed after implementing sprint planning, the reduced cycle time from continuous integration, and the 20% higher customer satisfaction scores from faster bug fixes. 

Results from inside your business speak louder than external case studies because they demonstrate what Agile can achieve in your specific environment.

Your dev team’s results give you a foundation, but Agile works across the entire business. When Santander rolled out iterative experiments in their business units, their customer loyalty went up 12%, account satisfaction rose 10%, and positive sentiment hit 90%.

The numbers from SEMRush show what happens when you apply Agile to marketing – their revenue went up 90% year-over-year in their top 10 new markets. These results demonstrate the business impact of Agile when you talk to other executives about wider adoption.

Engaging Teams Beyond Development

Your development team’s success gives you what you need to get other departments on board. Set up coffee meetings with department heads to talk through their workflow problems. Show them how Agile practices fixed similar issues in your dev team. Keep it simple – invite them to watch a sprint review or daily standup so they can see how it works.

When you’re bringing Agile to the wider business, you need two things: presentations for executives that focus on numbers and results, and workshops that teach teams how to apply Agile to their work. Build connections with other managers dealing with productivity issues – they’ll help spread Agile practices across departments. This isn’t about pushing technical processes – it’s about giving teams better ways to get work done.

Overcoming Common Objections To Agile

Your teams will object to adopting Agile. When they say ‘We’re not developers,’ point them to how Agile principles improve any workflow through iteration and feedback. The code doesn’t matter – getting work visible and adjusting course does.

When teams say ‘We’re too busy,’ use it to uncover their problems. Find out what work gets delayed and which processes waste time. Show how Agile fixes these specific issues rather than suggesting more work. For teams claiming ‘It won’t work here,’ start small. Run a pilot with a marketing campaign or hiring cycle. Let them use their own terms instead of ‘sprint.’ Keep the core Agile concepts but have teams adapt the details to match how they work.

Implementing Agile Across Departments

Here’s how to translate Agile practices into workflows that match how different departments work. 

Marketing can plan campaigns in two-week sprints and use data from customers and social channels to adjust direction. 

HR teams can test new policies with small groups and track hiring with visual boards. 

Sales teams can run weekly pipeline reviews to spot and fix problems while capturing what customers tell them.

Operations teams have a simple starting point – the visual workflow boards your dev team uses. Add quick morning meetings to coordinate work and find problems early, and you have a basic Agile setup that works for most operational teams.

When you set up Agile practices in other departments, change the words to match how they work. Marketing teams might want to call sprints ‘campaign cycles’. Operations teams might prefer ‘improvement reviews’ over retrospectives. What matters is measuring the results – track how much faster marketing launches campaigns or how many days HR saves in hiring. These numbers tell you if the new process works.

Sustaining Long Term Change

The results from your pilots give you what you need to spread Agile across your business. Set up meetings between teams that have implemented Agile and those who haven’t. Let teams show their numbers – Marketing’s faster campaign delivery times or HR’s shorter hiring cycles. Keep it simple and practical.

Teams learn from teams. Schedule informal sessions where departments can talk through what worked and what didn’t. This gives you the data you need to pick which department to bring into Agile next. Your approach gets better with each implementation as you learn what parts of Agile work in your business.

Pick a department to start with. Look for signs they’re already thinking about changing how they work. Set up a meeting with their manager and walk them through what you’ve learned from your dev team’s experience with Agile. That conversation will give you what you need to start bringing Agile into the rest of your business.

The Path Forward is Clear

Your development team’s success with Agile gives you everything you need to improve how your whole business works. The numbers tell the story – faster delivery, better results, and happier customers come from breaking work into small pieces, measuring what matters, and changing direction when needed. These aren’t just software practices – they’re better ways of working that can transform every department in your business.

You’ve seen what Agile can do in development. Now you have a clear path to bring those benefits to the rest of your business. Start with a coffee meeting with another department head. Show them your team’s results. Walk them through a sprint review. The sooner you start those conversations, the sooner your whole business can start seeing the benefits of Agile ways of working.

SoftwareSeni has been selectively evaluating tools across the AI landscape since ChatGPT was announced in late 2022. Our developers had experimented with LLMs even before that (gpt-3, the predecessor to GPT3.5 that was the original “ChatGPT”, was opened to unrestricted access in November 2021).

The tools themselves have started to improve rapidly over the last few months as newer State of The Art (SOTA) models and “reasoning” models have been released by all the major providers – OpenAI (makers of ChatGPT), Anthropic (Claude), Google (Gemini).

The SOTA models provided by these companies power almost every third party AI tool, particularly coding tools. And the effectiveness of those tools has grown along with the power of the models.

At the same time, both the model providers and the tool companies have matured to the point where they now have secure offerings.

The model providers have been using interactions with their models (either via their user interfaces like ChatGPT.com, Claude.ai and Gemini.google.com, or programmatically via their APIs) to gather more data to train their models on.

If you ever wondered what the little thumbs-up and thumbs-down icons under the responses in ChatGPT were for – that is it. Volunteer model training.

No business wants their IP or their customer data copied, used in training an AI, and possibly appearing in later outputs of that AI as a part of a response to some random person’s query.

Important industries could never be clients to these model providers or use any tool built on them. For example, finance and healthcare have stringent data handling requirements.

But there’s a lot of money in those industries, so now the model providers offer their paid services with privacy guarantees, including fine-grained permissions management at the enterprise level.

Tool providers, like Cursor, who make a code editor, have also made privacy part of their product and provide detailed explanations on how they interact with your code. This is part of the reason they are valued at $10 Billion, despite being a fork of the open source Visual Studio Code editor (created by Microsoft) and their reliance on third party SOTA models for generating code.

The AI tools SoftwareSeni uses

With our focus on software development we obviously have a huge interest in coding tools. Our teams work across a wide range of platforms, languages and frameworks, so we weren’t able to pick just a single solution.

We ended up going with Cursor and Github Copilot. They’re currently in competition to achieve feature-parity with each other, with Cursor being a little cleverer and a little more agentic at the moment. 

But Copilot has the advantage of running across multiple IDEs, meaning it is integrated into the tools our developers already use, like JetBrains IDE. Cursor is just Cursor – you use it in as part of their VS Code fork or not at all. 

Both tools provide strong privacy guarantees in their paid versions.

Away from coding, in general operations (recruitment, training, etc) we will be using ChatGPT. We also considered Claude, but OpenAI’s range of models and team features, as well as a bit of first-out-the-gate advantage, has made them our first choice.

There are differences between the platforms, but like the coding tools, everyone is racing towards feature parity. And with AI to write code for them, any gaps between competitors shouldn’t last long, right?

Choose your own tools

Our clients, particularly our extended software development team clients, can choose the tools they want developers to use. Or they can choose to have their developers continue to code traditionally if they aren’t comfortable with the privacy guarantees from the model and tool providers.

We do have active clients that have already moved their software development teams over to AI coding tools. 

Despite what you might hear or read about people rapidly building apps, “vibe-coding” their way to new products in days on their own, reliable, resilient, scalable software still requires professional developers and time. If, as they say, the devil is in the details, then software remains pretty much angel-free. Though testing and documentation does get quite a boost – two things developers love to neglect but make every project better.

Here is a pair of tweets that demonstrate the upside and downside of using AI coding tools without the necessary experience:

Is your team ready for AI?

The next few years are going to be very interesting. Interesting for the whole world, but especially interesting for everyone like us who is building a business around software. We’re going to be able to achieve more and move faster. Small teams are going to be able to take on projects that used to be out of reach. 

If you add AI coding tools to your team, we expect a few months in, if not sooner, you will start re-assessing your timelines and your product goals. 

Get in contact with us if you want to talk about an AI-powered software development team or project. We’d be happy to chat through the options and possibilities with you.

Finding qualified developers in Australia’s competitive tech market is quite the business challenge and has been for some time.

Whether you’ve lost a key team member or need to scale up for new projects, the limited talent pool creates bottlenecks that impact your entire operation.

The consequences are immediate and measurable: delayed projects, increased technical debt, and a team creeping towards burnout. Your business faces a difficult choice between accelerating hiring (risking poor cultural fit) or maintaining standards while watching your backlog grow.

We’re going to take a quick look at how developer shortages affect business operations and cover a solution that’s helping SMEs keep their development moving forward without digging themselves into a hole.

The Impact Of Developer Turnover

When a developer leaves, work stops. Your front-end developer hands in their notice after months of leading the new app interface project. That project stops. Your team has to pick up the work, the project falls behind schedule, and you miss releases.

Missed releases mean missed revenue. And that affects your quarterly targets.

Your remaining developers take on work outside their usual responsibilities. They work longer hours to cover the gaps. Quality drops as the team tries to maintain the pace. Bugs increase and technical debt grows.

The team’s performance suffers under the added workload. As Nichole Viviani notes, “For some employees, [losing a teammate] leads to frustration, resentment and burnout, and can prompt them to question whether they, too, should be looking for a new opportunity.” Team members start questioning their own roles when they see a colleague leave, affecting both morale and productivity.

The Business Impact Of Slow Hiring

When your business needs more developers, every day spent hiring costs you money. Strategic projects get put on hold while you compete with every other tech company for the same limited pool of talent.

Your product development timeline stretches from months into quarters. Your team maintains current systems instead of adding new features. This creates a problem in Australia’s developer market – slower development makes it harder to find developers. Developers want to work on teams that build new things. When development slows, finding developers gets harder, which slows development further.

Slow hiring blocks growth. When you can’t add developers to your team, you miss market opportunities. Your competitors release features while your development plans sit waiting for people to build them. Your product ideas stay stuck on whiteboards during the months you spend interviewing.

Meanwhile your infrastructure costs keep running. Your servers keep serving. Your software licenses keep billing. Your office space stays lit and air-conditioned. But without enough developers, you’re paying for capacity you can’t use.

Risks and Trade-offs

Running a development team with missing staff puts you in a constant juggling act between business needs and technical debt. You have to balance keeping your product moving forward against maintaining code quality, while managing the workload of your remaining developers.

The ‘bus factor’ measures how many people need to leave before your project stops working. It’s a measure of risk – the more knowledge that lives only in your developers’ heads, the higher that risk. When you run a small team, one developer leaving can stop work on key features because that knowledge leaves with them.

The recruitment process itself consumes resources – you pay agency fees and advertising costs while your developers spend hours interviewing candidates instead of writing code. This puts you in a position where you need to choose between fast hiring that risks bringing in someone who doesn’t fit, or maintaining your standards while watching project timelines extend.

The Extended Team Solution

Extended software development teams provide a solution that bypasses local market constraints. Your service provider employs and manages a team that works as part of your in-house development team. This gives you access to developers beyond the Australian market and reduces hiring time from months to weeks.

The numbers work out too – when you calculate the total cost of local employees including super and leave loading, extended teams cost less. This setup lets you bring in developers to keep projects moving while you work through permanent staffing.

Nearshore extended teams have the benefit of a large timezone overlap, which makes meetings, reviews, and general team orchestration easy. Plus, the members of an extended have an extra layer of management – the service provider manages the operational side for you, from setting up development environments to tracking team performance. This lets you focus on product direction and strategy.

Extended teams give you control over team size. You can add developers when projects need them and reduce numbers during quiet periods without HR complexity. You keep control of your IP and technical direction – your architects and tech leads make the decisions while the extended team provides the development capacity.

Taking Action For Your Business

Slow hiring processes in Australia’s tech sector block business growth and increase operational risks. When you lose developers or need to scale up, the time spent searching for talent leads to overworked teams and missed project deadlines. Your development plans end up sitting on whiteboards while competitors keep shipping features.

Extended teams provide a way around these constraints. By working with developers in overlapping timezones, you maintain your development velocity without the delays of local recruitment. The service provider handles operational management while you keep control of your IP and technical direction. You get the ability to adjust team size based on project needs, and your development processes continue without disruption.

Keep Your Business Moving Forward

It’s obvious. Delayed hiring creates a cascade of problems for your business. When developers leave or you need more developers to grow, the time spent searching for talent leads to overworked teams, missed deadlines, and stalled projects.

Extended teams offer a practical solution – you get skilled developers who work within or across your timezone, managed support that handles operational coordination, and the flexibility to scale your team size as needed. All while maintaining control of your IP and technical direction.

If your business is facing hiring delays or you want to prepare for future growth, consider how extended teams could work for you. The setup is straightforward and you could have new developers in place in as little as two weeks.

If you want to explore how this approach could keep your development moving forward get in touch with us. We’ve got plenty of clients who can tell you how our developers on their teams are powering their growth.