விரிவுரை முன்னோட்டம்

feature_list.json for AI Agents: WIP=1, Feature Status and verify.sh Verification

How to limit an AI coding agent to one task at a time (WIP=1): feature_list.json with feature states, task granularity, scripts/verify.sh as the only gate for passing status, strict vs weak verification.

WIP=1 task granularity feature_list. feature finite state machine gatekeeper
இந்த விரிவுரையில் நீங்கள் என்ன கற்றுக்கொள்வீர்கள்
  • Implement WIP=1 and watch the number of unfinished parallel changes fall.
  • Set up feature_list. with an honest finite state machine, instead of the agent marking its own homework.
  • Write verify.sh as the sole authority permitted to set a status to passing.

விரிவுரையின் தொடக்கம்

Twelve files, eight hundred lines, zero finished features

  • There was just one request: 'add sign-up'
  • Along the way, the agent 'helpfully' wired in email verification, bcrypt and a middleware refactor
  • The resulting code is tightly coupled, yet no scenario passes end-to-end
  • The next session opens this tangle from scratch

The request was exactly one thing — build sign-up. Here's how, in practice, it sprawled into six directions: the agent models the user, then decides email verification is needed, wires up a mail service, remembers password hashing still needs doing, pulls in a separate library, spots inconsistent error handling and refactors the middleware, and tidies up the test structure along the way. Six directions, each half-done, all tightly coupled together — and not a single end-to-end run. The next session opens this code with no idea where to begin.

Arithmetic, not metaphor

  • C — the agent's usable context capacity
  • k — the number of simultaneously active tasks
  • Each task gets roughly C/k of the reasoning resource
  • As soon as C/k drops below the minimum, no task gets seen through to completion

This isn't a metaphor, it's arithmetic. Call the agent's usable context capacity C, and the number of simultaneously active tasks k. Each task gets roughly C divided by k of the reasoning resource. As soon as that value falls below the minimum needed to carry a single task through to completion, nothing gets completed. The more tasks the agent juggles at once, the lower the chance that any one of them genuinely gets finished.

The trap: one request turns into six projects

  • ❌ The agent treats 'fixing things while we're at it' as a free action, and often does exactly that: pulling in an email service, bcrypt, a middleware refactor, test reorganisation — all within a single task
  • ✅ One request = one branch of work; anything noticed along the way goes into BACKLOG.md, not into the current scope

'I'll fix that while I'm at it' is the most expensive phrase in an agent's vocabulary. You ask for sign-up, and the agent — since it's already deep in the code anyway — fixes error handling while it's at it, adds email verification while it's at it, tidies up the tests while it's at it. Each of these 'while I'm at it' moves seems almost free to the agent. This is a trap people fall into constantly: scope creep costs the agent almost zero tokens, but the cost to the project is real. The right move is different: one request, one branch of work, and everything else noticed along the way gets recorded separately and left well alone.

முழு விரிவுரை சந்தா மூலம் கிடைக்கும்

முழு பதிப்பில் மேலும் 23 ஸ்லைடுகள் உள்ளன.

உள்நுழைந்து சந்தா பெற்ற பிறகு வீடியோ, அனைத்து ஸ்லைடுகள், குறிப்புகள், தேர்வுகள், AI ஆசிரியர் மற்றும் நேர்காணல் திறக்கும்.

உள்நுழை அல்லது பதிவு செய் சந்தா பெறு

விரிவுரை உள்ளடக்கம்

  1. Twelve files, eight hundred lines, zero finished features
  2. Arithmetic, not metaphor
  3. The trap: one request turns into six projects
  4. Myth: more code means more progress
  5. An agent has no brakes
  6. BACKLOG.md — a release valve for the 'while we're at it' impulse
  7. BACKLOG.md — a release valve for the 'while we're at it' impulse
  8. Scope lives outside — in a file
  9. Scope lives outside — in a file
  10. VCR: the metric that keeps the agent on a short leash
  11. WIP=1 is set. But who remembers what's already done?
  12. 💡 Not too wide, not too narrow: the right size for a feature
  13. Feature = session = commit = scenario: the size check
  14. WIP=1 is set. But who remembers what's already done?
  15. 📖 'The basket's nearly done, but there are bugs' — nearly done for whom?
  16. The feature list is the harness’s spine, not a memo
  17. Three fields without which a feature entry is just words
  18. A feature has exactly four states — and not one extra transition
  19. The passing gate: the agent can request it, but not declare it
  20. The feature list rules live in AGENTS.md too
  21. Two levels of checking: first the whole, then the part
  22. Two levels of checking: first the whole, then the part
  23. “The function exists” doesn’t mean it works
  24. A feature's journey from queue to passing: three places it can get stuck
  25. Step 4 of the end-to-end project: feature_list. for the payment backend
  26. The harness applies the brakes, not the agent

இந்தப் பாடம் தொடர்பான கட்டுரைகள்

இந்தப் படிப்பின் அனைத்து விரிவுரைகள்

  1. What Is Harness Engineering? Why AI Coding Agents Fail Because of the Environment
  2. Error Accumulation in AI Agents: Why 95% Per-Step Accuracy Fails by Step 10
  3. AI Agent Reliability Metrics: MTBF, MTTR, Blast Radius and Little's Law
  4. Why AI Agents Fail in Production: Five Failure Layers and a Diagnostic Loop
  5. What an AI Agent Harness Is Made Of: Instructions, Tools, Environment, State, Feedback Loops
  6. Repository Map for AI Coding Agents: Project Structure and State Without Memory
  7. AGENTS.md Best Practices: Why a Giant Instructions File Fails and How to Build a Router
  8. AI Agent Memory Between Sessions: Session Handoff, PROGRESS.md and Bootstrap Contract
  9. feature_list.json for AI Agents: WIP=1, Feature Status and verify.sh Verification
  10. Agent Says 'Done' but the Feature Is Broken: Verification Gap and Definition of Done
  11. All Tests Pass, Feature Is Broken: E2E Tests and Architecture Rules for AI Agents
  12. AI Agent Observability: Sprint Contracts, Evaluator Rubrics and Clean Session Exit
  13. Autonomous AI Coding Agent for Hours: Loop Engineering and Stop Conditions
  14. Graph Engineering for AI Agents: When a Single Loop Is Not Enough and You Need a Task Graph
  15. Building an AI Agent Harness From Scratch: AGENTS.md, feature_list.json, verify.sh and an Autonomous Loop