Cursor just raised $2.3 billion at a $29.3 billion post-money valuation. For an IDE. That’s more than the GDP of several small countries, for what looks like VS Code with better autocomplete.
But autocomplete misses the real story. These tools aren’t just helping you code faster. They’re enabling autonomous software development through architectural foundations that shift how development happens—from reactive code suggestions to proactive task delegation. The difference between a tool that suggests what you might type next and one that can queue overnight work while you sleep.
The technical foundations of the IDE wars rest on these architectural innovations—context management systems, standardised protocols, and autonomous orchestration capabilities that determine competitive positioning and vendor differentiation.
If you’re evaluating these tools for your team, you need to understand what’s actually under the hood. What makes an IDE “agentic” versus “AI-assisted”? Why does the Model Context Protocol matter? How do context windows constrain what these systems can do?
This article explains the technical components that differentiate these tools. Not marketing abstractions, actual architectural decisions that affect your productivity potential and risk profiles.
What Is an Agentic IDE and How Is It Different From AI Code Completion?
GitHub Copilot showed us AI could write syntactically correct code at scale. But Copilot is reactive. You write code, it suggests completions. You accept or reject. The AI doesn’t make decisions, doesn’t plan workflows, doesn’t coordinate changes across multiple files.
Agentic IDEs handle tasks autonomously. They plan workflows, execute changes across files, run terminal commands, and verify their work. The difference isn’t faster typing. It’s delegation.
Traditional AI tools require you to orchestrate every step. Agentic systems shift that responsibility. You express outcomes rather than instructions. The agent handles planning, execution, and adaptation by itself.
Ben Hall frames this well: delegation is a senior engineering skill. It requires deliberate teaching. You’re not training your team to accept or reject suggestions anymore. You’re training them to write specifications and review outputs. That’s a different skillset entirely.
Look at the concrete implementations. Cursor’s Agent Mode lets you specify a high-level requirement. The agent plans the approach, creates multiple files, writes tests, and verifies the implementation. Composer Mode gives you guided multi-file edits with less autonomy.
Windsurf specialises in large codebases through its Cascade feature. This automatically determines and loads relevant context—particularly valuable for monorepos with hundreds of files.
Google Antigravity treats AI as a development team rather than a coding assistant. Multiple agents work simultaneously on different tasks. Parallel execution.
You can queue tasks and let agents work overnight, then return to review completed pull requests. Fire-and-forget capability changes the supervision model. Instead of providing synchronous assistance, you supervise multiple concurrent AI developers working independently.
Compare that to Copilot suggesting individual function bodies. It doesn’t coordinate multi-file refactoring. It doesn’t plan. It reacts.
This aligns with existing team practices—pairing, design review, parallelising work. Agentic AI amplifies what good teams already do rather than replacing engineering judgement.
How Did We Get From Code Completion to Autonomous Agents?
GitHub Copilot launched in 2021 through collaboration between GitHub and OpenAI. It was built on OpenAI’s Codex, a fine-tuned GPT-3 trained on public repositories. Copilot demonstrated AI could write syntactically correct code at scale, but it was reactive only—no planning or multi-file coordination.
Several technical breakthroughs between 2023 and 2025 enabled the shift to autonomous agents.
Larger context windows—from 4K tokens to 200K+ tokens. This allowed models to understand entire codebases instead of single files and made multi-file reasoning and refactoring possible.
Tool use capabilities—LLMs learned to call external functions and APIs. Claude API introduced programmatic tool calling in 2024. This let agents execute terminal commands, read files, and run tests.
Chain-of-thought reasoning—models improved at planning multi-step tasks. They could decompose complex requirements into subtasks, self-verify, and iterate on failures.
Model Context Protocol standardisation—this solved ecosystem fragmentation by providing a unified interface for connecting AI to external tools and data. It enabled rapid ecosystem growth.
The standardisation occurred largely through the Model Context Protocol. It solved a problem with infrastructure. Cursor then demonstrated commercial viability of the agentic approach in 2024—a VS Code fork with agent capabilities plus inline autocomplete, Composer Mode for guided multi-file edits, and Agent Mode for full autonomy.
Now you’ve got multiple vendors implementing agentic capabilities—Cursor, Windsurf, Antigravity, VS Code with Copilot. Industry standardisation through the Agentic AI Foundation governing MCP.
The shift moves from AI-assisted coding to AI-delegated development.
What Is the Model Context Protocol and Why Does It Matter?
Anthropic describes MCP as a USB-C port for AI applications. That’s the right analogy. Before USB-C, every device had different connectors. Before MCP, every AI application built custom integrations for every tool and data source.
MCP provides a standardised protocol for AI-to-tool connections. Here’s the technical architecture: MCP uses a server-client model where servers expose data sources, tools, and workflows through a standardised interface. Clients—agentic IDEs, ChatGPT, Claude—consume these capabilities. The protocol specification defines communication format, authentication, and capabilities discovery.
The November 2025 spec release introduced asynchronous operations and statelessness. Non-blocking tool calls enable background agent work. Claude API added tool search optimisation to efficiently handle thousands of tools without performance degradation.
Real-world implementations show the value. A Slack MCP server lets agents search messages, post updates, and manage channels. A GitHub server provides repository access, pull request management, and issue tracking. Database servers allow agents to query SQL and NoSQL databases with permission controls.
Claude Code ships with 75+ MCP connectors. Ecosystem metrics show significant adoption—more than 10,000 active public MCP servers, over 97 million monthly SDK downloads, and major platform adoption including ChatGPT, Gemini, Microsoft Copilot, VS Code, and Cursor.
Why this matters for agentic IDEs:
Ecosystem network effects—any IDE supporting MCP instantly accesses 10,000+ integrations. You don’t build connectors yourself.
Enterprise adoption—IT teams can build internal MCP servers without vendor lock-in using a standard protocol rather than waiting for vendor support.
Interoperability—developers can switch IDEs without losing tool integrations.
Innovation velocity—third parties build connectors independently of IDE vendors.
The Agentic AI Foundation governs MCP development. It’s co-founded by Anthropic, Block, and OpenAI. Competitors collaborating on standards. Industry support from AWS, Microsoft, Google, Cloudflare, and Bloomberg signals the market choosing standardisation over proprietary fragmentation.
Security requires careful consideration. MCP servers implement their own authentication—OAuth, API keys, whatever fits their system. Agentic IDEs act as trusted clients with delegated permissions. Enterprise deployment requires careful permission scoping.
How Do Context Windows Affect Codebase Understanding?
A context window is the amount of text or code an AI model can process and remember in a single interaction. It’s measured in tokens—roughly 0.75 words per token.
Modern ranges vary significantly. GPT-4 handles 8K to 32K tokens, roughly 6K to 24K words. Claude 3 supports 200K tokens, about 150K words. Gemini Pro extends to 1 million tokens, approximately 750K words.
To put this in perspective, a 200K token window holds roughly 150,000 words—equivalent to about 750 medium-sized source files of 200 lines each, or a complete small to medium codebase.
This determines how much code the agent can comprehend simultaneously. And that’s a limitation.
Real-world codebases have millions of lines across thousands of files. Even 1 million token windows can’t hold complete codebase loading. So how do agents understand architecture without seeing everything?
The risk is epistemic debt—agents generating code without understanding context, creating knowledge gaps that become costly during debugging or modification. Fast code generation creates working code with missing understanding.
There are four architectural solutions for context management.
First, intelligent context retrieval. Codebase comprehension systems index the entire repository and retrieve relevant portions. Cursor analyses codebase structure and loads related files based on the task. Windsurf Cascade automatically determines and loads relevant context from large monorepos. Retrieval-Augmented Generation queries the index for relevant code snippets.
Second, spec-driven development. Requirements.md, design.md, tasks.md serve as contracts between humans and AI. Specifications survive context window limits and session boundaries. The agent reloads specs instead of rediscovering requirements—this enables consistent execution across multiple sessions.
Third, hierarchical understanding. Load summaries and interfaces first, detailed implementations second. Type definitions and API contracts provide architectural context. Dependency graphs guide file selection priority. Progressive detail: start broad, drill down as needed.
Fourth, persistent memory systems. These store institutional knowledge, previous decisions, and patterns across sessions. They transform agents from stateless tools into living systems of record, reduce repeated context loading overhead, and enable cross-session learning and consistency.
The practical implications for team workflows matter. You’ll need to decompose some operations that exceed context limits. Token budget awareness becomes part of planning. Specification quality matters because poor specs force agents to guess from limited context. Code organisation impacts how easily agents can navigate your codebase. Documentation becomes agent inputs—your README files and architecture docs aren’t just for humans anymore.
Performance and cost trade-offs exist. Larger context windows mean higher API costs per request. Context retrieval accuracy determines whether you avoid hallucinations. You’re balancing loading enough context for correctness against minimising tokens for cost. For enterprises, monthly costs scale with codebase size and query frequency.
How Do AI Coding Tools Learn to Generate Code?
Three learning mechanisms give models coding capabilities.
Pre-training provides foundation model capabilities. Large Language Models train on massive text corpora including public code repositories. OpenAI Codex was fine-tuned GPT-3 on GitHub public repositories.
This teaches programming language syntax and semantics, common patterns and idioms, library and framework usage, and code-comment relationships.
The limitation is generic knowledge—no specialisation for specific tasks or codebases.
Fine-tuning adds task-specific optimisation. It’s additional training on curated datasets for specific capabilities. Examples include tool use fine-tuning, where models learn to call APIs and interpret results. Code editing fine-tuning teaches surgical modifications versus generating from scratch. Test generation fine-tuning trains models to write comprehensive test suites.
Proprietary vendors have an advantage here. Cursor and Windsurf likely fine-tune models for IDE-specific tasks. This is why proprietary models exist—competitive differentiation through specialised capabilities.
In-context learning provides runtime adaptation. Models learn from examples and context provided in the prompt. Few-shot learning means giving two or three examples of desired behaviour and the model generalises from there.
For agentic IDEs this works by providing existing code as examples of project style, showing test patterns to match, and including architecture docs for consistency. It’s most powerful when combined with large context windows because you can include many examples.
Agentic capabilities require specific development approaches. Chain-of-thought training teaches models to explain reasoning steps before generating code. Tool use training teaches when and how to call external functions—file reads, terminal commands, API calls. Multi-step planning trains on task decomposition and execution sequences. Self-correction teaches models to verify outputs and iterate on failures. Critique and refinement training enables evaluating own work and improving iteratively.
Cursor Composer demonstrates this specialisation. During training, the model accesses production search and editing tools, using reinforcement learning to optimise tool use choices and maximise parallelism for interactive development.
Current models don’t learn from user corrections in real-time. The future direction involves personalised models that adapt to individual codebases and styles. Privacy considerations matter—is user code used for training or inference only? Enterprise requirement: guarantee no data leakage to public models.
How Do Checkpoint Systems Work in Autonomous Agents?
Autonomous execution creates new operational challenges. Git tracks code changes, but autonomous agents need more comprehensive rollback capabilities—not just code, but agent reasoning, tool outputs, and intermediate states.
Without checkpoints, agents proceed down incorrect paths without recovery mechanisms. For regulated industries, audit trails become compliance requirements.
Checkpoint systems capture four types of information:
Conversation context includes complete dialogue history between developer and agent, user specifications, agent clarification questions, and reasoning explanations. This enables recreating why decisions were made.
Tool call sequences record all external tool invocations—file reads, terminal commands, API calls—plus tool outputs and return values. This allows replay or rollback of agent actions.
Intermediate states capture partial code generations before final output, alternative approaches considered and rejected, and test results. This enables understanding agent decision-making processes.
Workspace snapshots record file system state at checkpoint boundaries. This allows full environment rollback including dependencies, configuration files, and build artefacts.
Implementation patterns vary. Git-based checkpoints create commits at task boundaries—familiar tooling but doesn’t capture conversation context. Database-backed state management stores complete agent state in SQLite or PostgreSQL—you can query and restore specific states but it requires a separate system. Hybrid approaches combine both: git for code changes, separate log files for tool calls and reasoning.
Anthropic introduced checkpoints for autonomous operation in Claude Code. These automatically save code state before each change. You can rewind to previous versions by pressing Esc twice or using the /rewind command.
Rollback capabilities take several forms. Automatic rollback triggers include test failures, build breaks, or lint violations—the agent detects failure and automatically reverts. Manual rollback controls let developers review changes and reject them. Selective rollback keeps some changes and reverts others.
Enterprise compliance requirements include audit trails showing complete records of who authorised what actions, approval gates before destructive operations, retention policies for checkpoint data, and access controls determining who can rollback or replay sessions.
Why Are Companies Building Proprietary Models Instead of Using APIs?
Cursor’s valuation is driven partly by proprietary Cursor-Fast and Cursor Composer models. The hypothesis: superior model performance equals stickier users equals defensible market position. If anyone can replicate features using OpenAI API, there’s no differentiation.
Technical advantages of proprietary models come in four categories:
Fine-tuning for specific use cases provides IDE-specific optimisations—multi-file editing, code search, test generation. Training on curated datasets of successful agent interactions. Cursor-Fast is optimised for low-latency inline completions.
Cost and latency control matters at scale. API costs scale linearly with usage through per-token pricing. Self-hosted models have high upfront cost but lower marginal cost at scale. Latency optimisation means deploying models geographically closer to users.
Data control and privacy become selling points. User code doesn’t leave vendor infrastructure—an enterprise selling point for guaranteed data residency. Competitive intelligence comes from learning from user interactions without leaking to OpenAI or Anthropic. Compliance becomes easier for meeting industry-specific requirements like HIPAA or SOC 2.
Feature velocity increases. You don’t wait for OpenAI or Anthropic to ship capabilities. Rapid experimentation with model architectures. Specialised features competitors can’t easily replicate.
Trade-offs exist. Proprietary approach downsides include requiring ML expertise—world-class AI researchers on staff. Infrastructure costs for GPU clusters, training pipelines, and model serving. Model quality risk: what if your model underperforms GPT-4 or Claude? Maintenance burden from continuously updating as the field advances.
API approach advantages include flexibility to switch to better models as released—GPT-5, Claude 4, whatever comes next. Lower upfront cost without training infrastructure investment. Leverage expertise from OpenAI or Anthropic’s research. Focus on product by dedicating more resources to IDE features versus model development.
Current market landscape shows different strategies. Proprietary: Cursor with Cursor-Fast, Windsurf with undisclosed models. API: VS Code with GitHub Copilot using OpenAI, Claude Code using Anthropic. Hybrid: most use both, proprietary for autocomplete and API for complex reasoning.
For buyers, the implications matter. Proprietary models mean vendor lock-in risk. API models mean dependency on third-party availability and pricing. You need to evaluate whether a vendor’s model quality justifies lock-in. Enterprise consideration: data residency requirements may dictate your choice.
How Do Autonomous Agents Orchestrate Multi-Step Tasks?
Task planning and decomposition starts with high-level goals. Give an agent “implement user authentication” and chain-of-thought planning breaks it into subtasks: design database schema, create API endpoints, implement JWT token generation, add authentication middleware, write integration tests.
Dependency analysis identifies prerequisite tasks—schema before endpoints. Artefact generation produces implementation plan documents for human review.
Spec-driven execution reads requirements.md and design.md for project context, generates tasks.md breaking down implementation steps, gets human approval, then works through tasks sequentially or in parallel based on dependencies.
Single-agent execution uses an action-observation loop: load relevant files, modify code based on subtasks, execute terminal commands, verify results, update state through checkpoints. The automated process begins with context loading, then code generation writes or modifies files, tool execution runs terminal commands to install dependencies or run tests, result verification checks for success or failure, and the agent either iterates or proceeds.
Error handling happens automatically. Test failures trigger automatic debugging attempts. The agent examines error messages and identifies likely causes. Limited retry budget—three to five attempts—prevents infinite loops. Escalation to human happens when retries are exhausted. Rollback option reverts to last checkpoint if stuck.
Google Antigravity uses parallel execution—treating AI as a development team with multiple specialised agents. Task distribution means different agents work on different features simultaneously.
Coordination challenges include file conflicts when two agents modify the same file, dependency violations when Agent B needs Agent A’s output, and resource contention from competing for compute or API quota.
Coordination mechanisms address these challenges:
Conflict detection and resolution monitors file access to detect when multiple agents target the same file. Locking mechanisms grant exclusive write access. Merge strategies combine compatible changes automatically. Human arbitration escalates incompatible changes.
Communication protocols let agents publish completed work to shared queues, subscribe to prerequisite task completions, and pass messages for coordinated operations. Agent A signals “users table created” and Agent B proceeds with endpoints.
Task redistribution handles blocking. If Agent A is blocked, reassign independent subtasks to Agent B. Load balancing across available agents. Priority queues for critical path tasks.
Visibility and control through dashboards showing all active agents and current tasks. Human intervention can pause, redirect, or cancel specific agents. Progress tracking shows percentage complete and estimated time remaining. Approval gates require confirmation before proceeding.
Safety mechanisms matter:
Human-in-the-loop controls provide configurable autonomy levels—full auto, supervised, or manual approval per action. Approval gates before destructive operations like database migrations, API calls, or deployments. Review artefacts including implementation plans, test results, and architectural decisions. Override capabilities let humans stop, modify, or redirect agent work mid-execution.
Workspace sandboxing executes agents in isolated VMs or containers, limiting the blast radius of errors. File system and network access restrictions. You can destroy and recreate the sandbox safely.
Audit trails and observability log all agent actions, tool calls, and reasoning. It’s a compliance requirement for regulated industries, a debugging aid to reproduce and analyse failures, and a performance optimisation tool to identify bottlenecks.
What Technical Capabilities Should You Evaluate When Choosing Agentic IDEs?
Evaluating agentic IDEs requires understanding six core technical capability categories.
Context management determines how much code the agent comprehends. Maximum context window size, retrieval accuracy for identifying relevant files, and monorepo support for enterprise-scale codebases. Windsurf Cascade specialises in large codebase comprehension.
Autonomy and control spectrum covers available autonomy levels. Can agents work completely independently? What about approval gate configuration for granular control? Rollback capabilities for safely undoing changes? Background execution for asynchronous or overnight work? Cursor Agent Mode versus Composer Mode offers different autonomy levels.
Multi-agent coordination enables parallel work. Can multiple agents work simultaneously? How are concurrent edits handled? Can you delegate to multiple agents efficiently? Google Antigravity demonstrates multi-agent parallelism.
Integration ecosystem matters for extending capabilities. MCP support for standardised servers, tool breadth for integrations available out of the box, and custom tooling through building internal MCP servers. Claude Code’s 75+ connectors demonstrate ecosystem maturity.
Model strategy affects performance and lock-in. Proprietary versus API—do you control model quality or depend on third parties? Model selection for choosing different models for different tasks. Cost predictability through fixed subscription versus usage-based pricing. Data residency for where code is processed.
Safety and compliance includes audit trails for logging agent actions, workspace isolation through sandboxed execution environments, permission controls to scope what agents can access or modify, and compliance certifications like SOC 2 or HIPAA.
Feature priority depends on organisation maturity.
Startups and small teams with 10 developers prioritise ease of use and learning curve, cost through subscription versus usage-based pricing, and speed to productivity. Less concern for compliance or multi-agent orchestration.
Mid-size companies with 50 to 200 developers need monorepo and large codebase support, integration with existing tools like CI/CD and issue tracking, team collaboration features, and moderate priority for audit trails and approval workflows.
Enterprises with 500+ developers require security and compliance certifications, data residency and privacy guarantees, granular permission controls, audit trails and governance, and support SLAs and uptime guarantees.
Vendor positioning varies significantly:
Cursor at $20 per month uses proprietary models with API options. Best for developer experience and flow state. Market leader.
Windsurf at $15 per month uses undisclosed proprietary models. Best for enterprise monorepos. Cascade context management.
Google Antigravity is free in beta. Uses Google Gemini API. Best for multi-agent parallelism.
VS Code with GitHub Copilot at $10 per month uses OpenAI API. Lowest barrier to entry for existing VS Code users.
Claude Code uses Anthropic Claude API with variable pricing. Over 75 connectors demonstrate ecosystem depth. Research platform.
Ask vendors these questions during evaluation: How do you handle codebases larger than your context window? What happens when agents make mistakes and what rollback capabilities exist? How do API costs scale with team size and usage patterns? Is our code used for model training and where is it processed? Can we export our configurations or skills if we switch vendors? What autonomous capabilities are planned in the next 12 months?
Understanding Technical Foundations Enables Better Decisions
Agentic IDEs differ from autocomplete through autonomous task execution versus reactive suggestions. MCP creates ecosystem network effects where standardisation enables rapid integration growth and vendor interoperability. Context windows remain a constraint where architectural solutions like Cascade and spec-driven development mitigate but don’t eliminate limitations.
Proprietary models create competitive moats but introduce vendor lock-in risks versus API flexibility. Autonomous orchestration requires safety systems where human-in-the-loop controls, rollback mechanisms, and audit trails become part of enterprise adoption.
Technical architecture directly impacts productivity potential and risk profile. Vendor evaluation requires understanding trade-offs between control versus flexibility, cost versus capability, and autonomy versus safety. Team skill requirements shift where specification writing and delegation become necessary competencies. Organisational readiness matters—are your workflows prepared for async agent execution?
The architectural choices made by these vendors shape what becomes possible in software development over the next several years. Understanding the foundations lets you evaluate claims with technical accuracy rather than accepting marketing narratives.
For comprehensive coverage of how these architectural choices shape the competitive landscape, including security implications, vendor selection frameworks, and implementation strategies, explore the full IDE wars analysis.
