28 posts found
Teams are moving from one big agent to many small agents. They need shared tool access and a clean way to talk to each other. Here's how MCP and A2A fit together without creating a mess.
Once you have many agents delegating work, debugging becomes the main cost. You need to answer basic questions fast: Which agent made the call? Which tools did it touch? What did it see? What did it cost? This is now a common pain point in 'agent mesh' style systems.
A practical guide to rolling out new agent versions without breaking production, using patterns from DevOps and feature flags.
How to catch agent bugs before they hit production by treating tools, prompts, and workflows like code.
Show, in concrete terms, how to let AI agents touch real infrastructure without turning your cluster into a sandbox accident.
A hands-on guide to designing CI/CD pipelines that treat agentic AI (multi-step, tool-using agents) as first-class deployable units, not magical black boxes.
How to build agents that fail safely instead of failing loudly.
How to stop your agents from doing unsafe or surprising things when they call tools like email, payments, or internal APIs.
How to stop agents from looping forever, burning tokens, and slowing everything down.
How to see what your agent actually did, step by step, and debug it like normal software. A practical guide to logging, replaying, and debugging AI agent workflows.