A hands-on implementation guide for building a policy-controlled AI agent harness with FastAPI, tool allowlists, approval gates, validation, audit logging, and a Dev.to RunKit demo.
This blog is the continuation from the previous blog harness-design-theory which is the harness design principles in theory.
The theory is useful, but it is not enough.
A bank does not need a chatbot that can randomly call Jira, GitHub, Slack, AWS, and Confluence.
A bank needs a controlled agent harness.
The model can reason.
A practical engineering guide to agent loops, harnesses, tool use, guardrails, observability, and security controls for…
A practical security architecture for governing employee AI usage and building a production AI agent with identity, permissions,…
Explore the concept of agent harness in AI, its components, and its role in software supply chain security. Learn about the…
It pays to know which tasks best fit which kinds of harnesses — and why it matters when they’re used at enterprise scale
Most AI demos look impressive until you ask a simple question: What is actually happening under the...
There's a difference between leveraging AI and relying on it. A framework for shipping agent-generated code with the judgment and…
What even is the harness in AI?
Agent harnesses, like OpenClaw, are changing how we build and run AI models
You gave your AI agent real tools. Here's the 4-part control layer it's missing + the Judge Layer implementation guide
How to Build an Advanced Agentic AI System with Planning, Tool Calling, Memory, and Self-Critique Using OpenAI API
Add a Specialized Deep Research Skill to Agent Harnesses | NVIDIA Technical Blog
