You’re probably facing the same question a dozen times a day: should I let AI write this code or do it myself?
The promise is speed. The worry is losing your edge. And somewhere in between is the nagging concern that you’re spending more time wrestling with prompts than you would’ve spent just coding the thing.
The reason this decision feels hard is that task selection drives everything else in AI delegation. Get it wrong and you’ll waste time on verification or build verification debt that comes back to bite you later. Get it right and you’ll maintain your mental models while shipping faster.
This practical guide explores delegation as orchestrator role practice—the daily implementation of identity shift that defines modern development. For context on how this fits within the broader transformation, see our guide on tactical decisions in strategic context.
This article lays out the decision criteria, trust-building progression, and verification strategies that let you delegate confidently without losing your coding chops.
What is a delegation framework for AI coding tasks?
A delegation framework is a set of decision criteria that tells you which coding tasks to hand off to AI and which to code yourself. It’s got four parts: task characteristics (can you verify it?), effort ratio (will prompting take longer than coding?), trust calibration (how fluent are you with the tool?), and verification planning (how will you check the output?).
The output is simple: a yes/no decision with a verification plan attached.
Think of it like delegating to a developer. You’d consider their strengths, the task’s complexity, how you’ll review their work, and whether explaining the task will take longer than doing it yourself. Delegation to AI is just as nuanced as delegating to a coworker.
The sheer variety of what AI can do makes it difficult to figure out what it should handle in any given situation. Your framework needs heuristics—quick decision rules you can apply in the moment.
How do I determine which coding tasks to delegate to AI?
Run every task through four filters:
Verifiability: Can you quickly validate correctness through tests, type checking, or inspection? High verifiability means delegation is safer. If the task has clear pass/fail criteria, AI can probably handle it.
Stakes: What happens if an error slips through? Low-stakes work like test scaffolding is perfect for delegation. High-stakes security code requires caution. The cost of being wrong determines how conservative you need to be.
Complexity: Well-defined tasks work great for AI delegation because LLMs excel at filling in the blanks with plausible defaults. But tasks requiring system-wide context or architectural judgement need human oversight.
Familiarity: Only delegate within domains where you can supervise the output. If you can’t verify correctness because you don’t understand the domain, you can’t delegate safely. That’s how you end up accepting code you can’t maintain.
Effective delegation requires skills enabling effective delegation—particularly context articulation and orchestration competencies that let you communicate intent clearly and supervise output strategically.
When factors line up—high verifiability plus low stakes—delegate confidently. When they conflict—high verifiability but high stakes—default to the conservative approach.
Some practical examples:
Delegate: Test generation, boilerplate CRUD operations, documentation, simple refactoring, data model scaffolding.
Don’t delegate: Security logic, architectural decisions, unfamiliar technology stacks, complex business rules, anything you can’t verify.
Here’s a useful pattern: instead of asking AI to analyze 1,000 log files one by one, ask it to write a script that automates the analysis. Use AI as a toolsmith, not a grunt worker.
Why does task verifiability matter for delegation decisions?
Verifiability determines how quickly and confidently you can validate AI output. It directly affects both risk and return on delegation.
The problem is this: generating code is one thing; ensuring it’s robust, secure, and correct is another challenge. Generation takes seconds. Validation can take longer than coding manually.
And there’s a verification gap. Research shows that while 96% of developers don’t fully trust AI-generated code, only 48% always check it before committing. Werner Vogels calls this disconnect “verification debt”—and it accumulates just like technical debt.
Highly verifiable tasks—unit tests, typed interfaces, pure functions—provide fast feedback loops. You’ll know immediately if output is correct. Run the tests, check the types, inspect the logic. Done.
Low verifiability tasks—complex business logic, UI interactions, performance-sensitive code—require expensive manual review or time-consuming integration testing. This is where verification debt piles up if you skip the hard work.
Here’s the calculation: verification cost (time to write tests plus time to review plus time to fix issues) versus manual implementation time. If verification takes more than 50% of the time you’d spend coding manually, reconsider delegation.
The risks that make verification necessary:
- Logical flaws: Code may work in common scenarios but fail on edge cases
- Security vulnerabilities: AI has no understanding of secure coding practices and can replicate insecure patterns like SQL injection
- Performance inefficiencies: Generated solutions prioritise functionality over optimisation
- Architectural drift: AI lacks understanding of long-term system vision
Low verification cost justifies delegation even with moderate error rates. High verification cost requires near-perfect output to make delegation worthwhile.
Understanding validation criteria in delegation decisions helps you determine when to trust AI output versus applying deeper verification.
How do I build mental models while using AI coding assistants?
The paradox of supervision: you need expertise to verify AI output, but relying too much on AI can erode that expertise.
Peter Naur called this “theory building”—programming is really about forming a theory of how systems work. This mental model is the real product, not the code itself.
Experienced developers take 19% longer to complete tasks when using AI tools, despite expecting speed gains. Why? Because articulating their well-developed mental models to AI is slow. Meanwhile, over-reliance can short-circuit the feedback loop that builds intuition for junior developers.
So how do you maintain mental models while delegating?
Reserve foundational tasks for manual coding. Core domain logic, algorithms, architectural decisions—these preserve deep understanding. Keep a “core competency tasks” list that never gets delegated.
Study AI output during verification. Don’t just check correctness. Understand why the approach works, what alternatives exist, what edge cases matter.
Use AI as a learning accelerator. Ask AI to teach concepts and break down logic. Request explanations alongside code. Prompt for trade-off analysis.
Iterate rather than accepting first output. Back-and-forth refinement deepens your mental model more than accepting whatever AI generates initially.
Red flags that you’re in trouble:
- Accepting code you don’t fully understand
- Inability to debug AI-generated output without re-prompting AI
- Discomfort modifying generated code
- Declining confidence in manual coding abilities
If any of those apply, reduce your AI delegation immediately. Code manually for a week. The productivity hit is worth preserving your expertise.
What is the trust progression when delegating to AI tools?
Trust calibration isn’t binary. It’s a progression through four stages, each with different delegation boundaries and verification strategies.
Stage 1: Sceptic. You verify everything. Limited delegation scope—mostly documentation and boilerplate.
Stage 2: Explorer. You’re experimenting with broader delegation while maintaining verification. You categorise task types by reliability.
Stage 3: Collaborator. You iterate fluidly with AI as a peer. You accept first output on familiar patterns but still verify.
Stage 4: Strategist. You delegate confidently based on task characteristics. Verification becomes strategic rather than exhaustive.
Successful verifications build confidence. Failures recalibrate boundaries. You can regress to earlier stages with new tools or unfamiliar domains—that’s normal.
The data shows why trust matters. Only 3.8% of developers fall into the “low hallucinations, high confidence” ideal scenario. Meanwhile, 76.4% are in “high hallucinations, low confidence” territory—this reduces adoption and ROI.
As your fluency develops, your delegation boundaries expand.
How do I verify AI-generated code effectively?
Verification needs to be tiered based on stakes and verifiability. Not every task deserves the same level of scrutiny.
Tier 1: Automated validation (always run). Unit tests, integration tests, linters, type checkers, security scanners, build validation. Before any human reviewer looks at a pull request, the code must pass through automated checks.
Tier 2: Structural review (5-10 minutes). Code readability, design pattern alignment, maintainability. Does this follow our conventions?
Tier 3: Behavioural testing (15-30 minutes). Manual functionality testing, edge case exploration, error handling validation.
Tier 4: Integration validation (30-60 minutes). Cross-system validation, data flow verification, API contract compliance.
Tier 5: Security audit (1-2 hours). Threat model review, input sanitisation, authorisation logic. For anything touching authentication, payments, or user data.
Allocate verification depth based on task stakes:
- Low stakes: Tier 1-2 only
- Medium stakes: Tier 1-3
- High stakes: Tier 1-4
- Sensitive: All tiers
The “vibe then verify” workflow works like this: fast generation followed by rigorous validation. You get speed with safety. Just don’t skip the verification step—that’s how verification debt accumulates.
How do I balance delegation speed with verification effort?
The delegation ROI comes down to an effort ratio: (prompt engineering time plus context provision plus verification time) versus manual implementation time.
Delegate when total AI workflow time is less than 70% of manual coding. Above that threshold, you’re not gaining enough to justify the overhead.
The cold start problem hits hard on unfamiliar tasks. Writing comprehensive prompts, providing codebase context, explaining constraints—this can exceed manual coding time. That’s the learning curve.
But the cold start improves as you build reusable context. You develop context artefacts—project overviews, coding standards documents—that you reference in prompts. The investment pays off through faster subsequent delegations.
Task categorisation helps:
- Never delegate: High stakes plus low verifiability
- Always delegate: Low stakes plus high verifiability
- Evaluate per instance: Mixed factors requiring judgement
The opportunity cost matters too. Time saved on delegated tasks enables work you’d otherwise deprioritise—fixing papercut bugs, updating documentation, refactoring technical debt.
How do I maintain coding expertise while delegating to AI?
Strategic non-delegation boundaries preserve your expertise while capturing delegation gains.
Identify core competencies. What skills define your professional value? Those stay manual. If you’re a backend specialist, keep implementing your algorithms and domain-specific logic manually. Let AI help with peripheral work.
Alternate delegation rhythms. Run “AI acceleration weeks” followed by “manual mastery weeks.” High-delegation periods boost productivity. Manual coding sprints maintain hands-on skills.
Treat verification as deliberate practice. Don’t just check correctness. Refactor AI code to internalise patterns. Identify improvements. Implement alternative approaches.
Delegate laterally, code deeply. Use AI to work outside your core expertise—this enables full-stack capabilities. A backend specialist can delegate frontend implementation and verify correctness through testing without deep CSS knowledge. AI accelerates breadth. Manual coding maintains depth.
Teach others. Explaining delegation strategies to teammates reinforces your own understanding.
Treat AI as a very eager junior developer that’s super fast but needs constant supervision and correction. That framing helps. You wouldn’t let a junior implement security logic unsupervised. Same applies here.
Monitor for skill atrophy. Can you still implement core algorithms from scratch? Debug complex issues without AI assistance? If any of those feel shaky, increase manual coding time.
Revisit your “never delegate” list quarterly. Boundaries should expand strategically, not automatically. You need the speed to ship and the expertise to verify.
What are practical delegation heuristics I can use immediately?
These quick decision rules help you make delegation calls in the moment:
1. “If I can write a passing test first, I can delegate the implementation”. Testability signals verifiability.
2. “Delegate boilerplate, code the business logic”. CRUD operations, data models, API scaffolding—delegate. Domain rules, complex workflows—manual implementation.
3. “If explaining the task takes longer than coding it, do it myself”. Catches the cold start overhead.
4. “Never delegate what I can’t verify”. Only delegate tasks where you can confidently assess output quality.
5. “Delegate laterally, code deeply”. AI accelerates work outside your speciality. Manual coding preserves core expertise.
6. “Generate options, choose direction”. Prompt AI for alternatives. Apply human judgement to select the path.
7. Stakes-based filter. Production security code equals manual. Development tooling equals delegate.
8. Familiarity threshold. Delegate only in domains where you have mental models for supervision.
9. Iteration tolerance. If a task requires more than three AI rounds, code manually.
10. Learning mode. When building mental models, code manually. The understanding is worth more than time saved.
Keep these heuristics accessible—print them out, add them to your IDE, or create a decision checklist.
Moving Forward with Delegation
Delegation isn’t about replacing your coding skills. It’s about applying them strategically—using AI to handle verifiable, low-stakes tasks while preserving expertise through deliberate practice on core competencies.
The heuristics in this guide provide tactical decision criteria for daily work. As you build fluency, these decisions become instinctive. You’ll develop intuition for which tasks benefit from delegation and which require hands-on coding.
For teams looking to implement these patterns at scale, explore scaling delegation patterns organisationally and workflow design across teams. Individual delegation tactics require organisational support to capture full productivity gains.
This tactical framework sits within delegation within comprehensive transformation—understanding where these decisions fit in the broader developer evolution helps contextualize why delegation boundaries matter.
Start with one heuristic. Apply it consistently. Refine based on outcomes. Build from there.
FAQ Section
What’s the difference between “vibe coding” and strategic delegation?
Vibe coding means accepting AI output without verification—fast but risky, accumulating verification debt. Strategic delegation combines generation speed with verification (“vibe then verify”), applying risk-based review depth matching task stakes. The question isn’t whether to verify. It’s how much.
How do I know if I’m in the “paradox of supervision” trap?
Warning signs: accepting code you don’t fully understand, inability to debug AI output without re-prompting AI, discomfort modifying generated code, declining confidence in manual coding abilities. If any of those apply, implement deliberate practice boundaries and increase manual coding time.
Which coding tasks should I never delegate to AI?
Never delegate: tasks outside your verification capability (unfamiliar domains), high-stakes security code without expert review, architectural decisions requiring system-wide context, core competency tasks that define your professional expertise, anything where context provision exceeds manual implementation time.
How long does it take to move from sceptic to strategist in trust progression?
Highly variable. Sceptic to Explorer takes 2-4 weeks of daily use. Explorer to Collaborator takes 2-3 months of practice. Collaborator to Strategist takes 6-12 months of strategic experimentation. Progression depends on delegation frequency, domain complexity, deliberate practice, and tool familiarity. Regression when switching tools or domains is normal.
Can I delegate code review itself to AI?
Partially. AI can identify style violations, suggest refactors, flag potential bugs. But architectural assessment, maintainability judgement, business logic correctness, and security review require human expertise. Use AI to accelerate review, not replace it. The reviewer remains accountable for quality.
How do I handle the “cold start problem” when delegating?
Build reusable context artefacts (project overviews, coding standards documents) referenced in prompts. Use AI to generate context from existing codebase. Start with tasks requiring minimal context (isolated utilities, tests). Invest in context provision only for recurring task types. Calculate break-even: is upfront context cost justified by future delegation efficiency?
What if AI generates code that works but I don’t understand how?
Red flag requiring action. Study the code—research unfamiliar patterns, trace execution mentally, add debugging to understand behaviour. Prompt AI for explanation. Refactor to a style you understand while preserving functionality. Consider re-implementing manually to build mental model. If understanding remains elusive, reject the output. You can’t maintain what you don’t understand.
How do I calibrate trust for different AI coding tools?
Treat each tool separately. GitHub Copilot excels at local completions. Claude Code handles complex multi-file tasks. Cursor integrates codebase context well. Track success rates per tool per task type. Build tool-specific delegation heuristics—”Copilot for boilerplate, Claude for refactoring, manual for architecture.” Trust calibration is tool-specific and context-dependent.
Should I delegate more as AI models improve?
Continuously renegotiate delegation boundaries as capabilities advance. Periodically revisit “never delegate” tasks to test current model performance. However, maintain core competency preservation regardless of AI capability—expertise remains valuable for verification, architectural decisions, and career resilience. Expansion should be strategic, not automatic.
How do I explain my delegation workflow to teammates or managers?
Frame as risk management: “I delegate tasks where I can verify output confidently and quickly, maintaining quality while gaining speed. High-stakes or unfamiliar tasks stay manual to ensure expertise and accuracy.” Share your heuristics, demonstrate verification process, track time savings. Position as professional judgement, not laziness.
What’s the relationship between delegation and “full-stack” capabilities?
AI delegation enables lateral expansion. You can work outside core expertise by delegating implementation while applying domain-general skills (verification, architecture, problem decomposition). Example: backend specialist delegates frontend implementation, verifying correctness through testing and functional review without deep CSS expertise. AI accelerates breadth. Deliberate practice maintains depth.
How do I document delegation decisions for future reference?
Maintain a lightweight log: task description, delegate versus manual decision with rationale, verification approach used, outcome (accepted/modified/rejected), time estimates (prompt plus verify versus manual estimate). Review quarterly to refine heuristics. Pattern recognition improves delegation accuracy over time.
