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.
- 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.
पाठ की शुरुआत
'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.
पूरा पाठ सदस्यता के साथ उपलब्ध है
पूर्ण संस्करण में 19 और स्लाइड्स हैं।
वीडियो, सभी स्लाइड्स, नोट्स, टेस्ट, AI ट्यूटर और इंटरव्यू लॉगिन करने और सदस्यता लेने के बाद खुलेंगे।
लॉग इन या रजिस्टर करें सदस्यता लेंपाठ की सामग्री सूची
- 'Just write a longer prompt'
- Myth: a harness = a prompt file
- A kitchen with five stations
- Stations 1–2: instructions and tools
- Stations 3–4: environment and state
- Station 5: feedback — the pass
- Where to start: 5 → 1 → 4 → 3 → 2
- ❓ Quick check: which station is your weakest?
- 💡 Diagnosis: how to tell what's broken
- Why different teams all follow the same path
- 🛠 What this looks like in real teams
- How to measure a component's value
- A zero drop ≠ 'the component is useless'
- Ablation pitfalls: it's not the place to start
- Constrain, don't micromanage
- A map, not a textbook
- Your harness rots just like code
- Where an agent's work leaks when one link breaks
- Read the diagram as a loop, not a list
- 30 minutes on the weakest station of your harness
- Next up: your first AGENTS.md for a payments backend
- A harness is a loop, not a file
इस व्याख्यान से जुड़े लेख
- Harness engineering क्या है: AI एजेंट को काम का उपकरण कैसे बनाएँ
- Harness vs context engineering: harness engineering prompt और context engineering से कैसे अलग है
- AI एजेंट का हार्नेस: यह क्या है और इसके हिस्से कौन-से हैं
- AI एजेंट के हार्नेस की शॉर्टकट शीट: हिस्से, फ़ाइलें, समस्याएँ और शुरुआत की चेकलिस्ट
- Harness engineering के इंटरव्यू सवाल: AI इंजीनियर के लिए 22 सवाल और जवाब
इस कोर्स के सभी पाठ
- What Is Harness Engineering? Why AI Coding Agents Fail Because of the Environment
- Error Accumulation in AI Agents: Why 95% Per-Step Accuracy Fails by Step 10
- AI Agent Reliability Metrics: MTBF, MTTR, Blast Radius and Little's Law
- Why AI Agents Fail in Production: Five Failure Layers and a Diagnostic Loop
- What an AI Agent Harness Is Made Of: Instructions, Tools, Environment, State, Feedback Loops
- Repository Map for AI Coding Agents: Project Structure and State Without Memory
- AGENTS.md Best Practices: Why a Giant Instructions File Fails and How to Build a Router
- AI Agent Memory Between Sessions: Session Handoff, PROGRESS.md and Bootstrap Contract
- feature_list.json for AI Agents: WIP=1, Feature Status and verify.sh Verification
- Agent Says 'Done' but the Feature Is Broken: Verification Gap and Definition of Done
- All Tests Pass, Feature Is Broken: E2E Tests and Architecture Rules for AI Agents
- AI Agent Observability: Sprint Contracts, Evaluator Rubrics and Clean Session Exit
- Autonomous AI Coding Agent for Hours: Loop Engineering and Stop Conditions
- Graph Engineering for AI Agents: When a Single Loop Is Not Enough and You Need a Task Graph
- Building an AI Agent Harness From Scratch: AGENTS.md, feature_list.json, verify.sh and an Autonomous Loop