Opleiding: Agent Harness Engineering

Better prompts don't scale. Learn how harness engineering makes AI agents reliable in production.

As AI agents become increasingly autonomous, better prompts alone are no longer enough. Prompts are suggestions agents can ignore or forget between sessions. Reliable AI systems are built through strong harnesses—the infrastructure layer that wraps an AI agent, enforces behavior, and validates results.
This training solves three real problems: agents claiming victory too early even when tests fail, losing context and repeating work between sessions, and building more than was asked because scope is undefined.
A harness has five subsystems working together:
- Instructions — What the agent should do, and in what order (CLAUDE.md, ADRs, domain language, progressive disclosure)
- State — What's been done, what's in progress, what's next (progress logs, feature lists, git history, session continuity)
- Verification — Only passing tests count as evidence (linting, mutation testing, E2E runs, no early victories)
- Scope — One feature at a time, no overreach (feature lists, explicit done definitions, constraint enforcement)
- Lifecycle — Initialize in an isolated sandbox before work, clean up after (health checks, file boundaries, permission controls, …