Agentic AI Harness Patterns
What is an AI agent harness, and why do ten named patterns keep it out of trouble? A field guide built on real incidents, a running example, and an interactive playground for every pattern.
Course modules
01Start here: what a harness is, and why patterns
Why the model is the small part of an AI agent, what the harness around it does, and why ten named patterns beat inventing your own every time.
The Tool Gateway
One checkpoint between the model and every tool it can call: is it registered, valid, permitted, within limits, and logged?
The Permission Boundary
You cannot stop a model being fooled. You can decide how much damage a fooled model can do.
The Approval Gate
Pause before actions that cannot be undone, save the state, and let a human decide.
The Sandboxed Runtime
When an agent writes and runs code, run it somewhere that cannot hurt you.
The Circuit Breaker
Stop calling something that is failing, give it time to recover, and test it carefully before trusting it again.
The Context Boundary
Treat everything the model reads as either instructions you wrote or data someone else wrote, and never let the second act like the first.
The Trace Pipeline
Record every step an agent takes as a structured trace, so you can see what it did, why it was slow, and what it cost.
The Shadow Evaluation Harness
Test a new prompt or model on real traffic without any user seeing the result, and block the release on regressions.
The Cost and Rate Governor
Give every agent run a budget and a step cap, because a stuck loop is the most expensive bug you can ship.
The Fallback Ladder
Degrade in planned steps, from the best answer to a safe one, instead of falling off a cliff.
Where to go next
AI Agents — From Basics to Multi-Agent Systems
Build AI agents from scratch — single-agent patterns, multi-agent pipelines, memory, observability with Langfuse, and a complete hands-on project. Covers LangGraph, CrewAI, AutoGen, and Agno with real code examples.
AI Ops
The difference between a demo and a product that survives real users: monitoring, cost control, security, guardrails, and rollback. The operational layer that keeps AI systems safe, affordable, and trustworthy in production.