The latest articles on DEV Community by Karim (@baidarkarim). https://dev.to/baidarkarim https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3889410%2F1eacba4d-4ee2-4534-9925-663411650b05.png https://dev.to/baidarkarim en Karim Mon, 20 Apr 2026 17:19:45 +0000 https://dev.to/baidarkarim/agent-consistency-a-python-consistency-layer-for-multi-agent-workflows-4lfl https://dev.to/baidarkarim/agent-consistency-a-python-consistency-layer-for-multi-agent-workflows-4lfl <p>I keep seeing AI agent workflows claim “task completed” even when the outcome was never actually verified.</p> <p>I’m curious how people here deal with:</p> <ul> <li>stale state between agents</li> <li>incomplete handoffs</li> <li>outcome verification</li> <li>workflows that look clean in logs but are still wrong</li> </ul> <p>Are you solving this with tests, traces, guardrails, custom validators, or something else?</p> <p>I built a small MIT-licensed Python package around this problem called agent-consistency.<br> Repo/demo here: <a href="https://github.com/karimbaidar/agent-consistency-refund-demo" rel="noopener noreferrer">https://github.com/karimbaidar/agent-consistency-refund-demo</a></p> <p>It’s not a framework replacement.<br> It’s a consistency layer meant to catch stale state, broken handoffs, and false success.</p> <p>I’d love blunt feedback on where this approach breaks down.</p> ai multiagent llm opensource