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.
Início da aula
'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.
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Mais 19 slides na versão completa.
O vídeo, todos os slides, anotações, testes, tutor de IA e entrevista ficam disponíveis após entrar e assinar.
Entrar ou cadastrar-se AssinarConteúdo da aula
- '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
Artigos sobre esta aula
- O que é harness engineering: como tornar o agente de IA uma ferramenta de trabalho
- Harness vs context engineering: a diferença entre harness, engenharia de contexto e engenharia de prompt
- Harness do agente: o que é e do que é feito
- Cheatsheet do harness do agente: componentes, arquivos, problemas comuns e checklist de execução
- Perguntas de entrevista sobre harness engineering: 22 perguntas com respostas para engenheiros de IA
Todas as aulas do curso
- 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