விரிவுரை முன்னோட்டம்

What an AI Agent Harness Is Made Of: Instructions, Tools, Environment, State, Feedback Loops

Five harness engineering subsystems: instructions (AGENTS.md), tools, environment, state and feedback loops. Rollout order, ablation testing of each component's value at a fixed model, and the flow diagram between subsystems.

the five harness subsystems ablation measurement adoption curve
இந்த விரிவுரையில் நீங்கள் என்ன கற்றுக்கொள்வீர்கள்
  • Break your harness down into its five subsystems and identify which one is missing.
  • Learn to measure the contribution of a specific component using an ablation experiment rather than by eye.
  • Draw up a prioritised implementation plan based on the returns curve.

விரிவுரையின் தொடக்கம்

'Just write a longer prompt'

  • The team spends a week writing a five-page AGENTS.md
  • The agent still won't run the tests by itself
  • Every session starts with fixing the environment
  • It looks like the model's fault — but it isn't

The team decides: since the agent keeps getting things wrong, everything needs describing in more detail — so they spend a week writing a five-page instruction document, listing libraries, tone of voice and the tech lead's favourite patterns. The agent reads it once, and still doesn't run the linter before committing — because it physically can't run the linter: it has no shell access with the right permissions. The problem isn't the length of the prompt. The problem is that instructions are only one of the five subsystems of a harness, and if the other four aren't in place, no amount of text will save you.

Myth: a harness = a prompt file

  • 🤔 Myth / Common confusion: 'I've got a harness' = 'I've got a prompt file'
  • ✅ In reality: a harness is five distinct subsystems, and instructions are just one of them
  • 📌 Remember: a shelf full of recipes is not yet a kitchen

The phrase 'I've got a harness' almost always means 'I've got a prompt file' — an AGENTS.md or a CLAUDE.md of some kind. But that isn't a harness, any more than a fridge full of ingredients is a restaurant. You've got the ingredients, but there's no stove, no knives, no way to taste-check a dish, and no quality-control system. A proper harness is five working parts, and missing any one of them doesn't make things 'a bit worse' — it creates its own distinct, predictable class of failures that will repeat again and again.

A kitchen with five stations

  • A harness = five subsystems working together
  • A missing part isn't 'slightly worse' — it's a systematic failure
  • Next up: one station of this kitchen at a time

Take away just one station from a professional kitchen, and it either never opens or ends up feeding customers on a wing and a prayer. A kitchen has five stations: the recipe shelf, the knife station, the stove, the prep counter, and the pass where quality control happens. It's exactly the same with a harness: instructions, tools, environment, state and feedback only work together, and a weak station can't be made up for by a strong one.

முழு விரிவுரை சந்தா மூலம் கிடைக்கும்

முழு பதிப்பில் மேலும் 19 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. 'Just write a longer prompt'
  2. Myth: a harness = a prompt file
  3. A kitchen with five stations
  4. Stations 1–2: instructions and tools
  5. Stations 3–4: environment and state
  6. Station 5: feedback — the pass
  7. Where to start: 5 → 1 → 4 → 3 → 2
  8. ❓ Quick check: which station is your weakest?
  9. 💡 Diagnosis: how to tell what's broken
  10. Why different teams all follow the same path
  11. 🛠 What this looks like in real teams
  12. How to measure a component's value
  13. A zero drop ≠ 'the component is useless'
  14. Ablation pitfalls: it's not the place to start
  15. Constrain, don't micromanage
  16. A map, not a textbook
  17. Your harness rots just like code
  18. Where an agent's work leaks when one link breaks
  19. Read the diagram as a loop, not a list
  20. 30 minutes on the weakest station of your harness
  21. Next up: your first AGENTS.md for a payments backend
  22. A harness is a loop, not a file

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