MLflow
@MLflow
The open source developer platform to build AI applications and models with confidence.
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Teams keep adding agent harnesses. Claude Code, Codex, Cursor, and the rest each keep their own conventions, so observing a run across them is messy.
@2twitme walks through that harness layer, then how @omnigent_ai puts coding agents and custom agents behind one common API. From there, @opentelemetry traces land in MLflow so you can see how the agent actually behaved.
Watch the full walkthrough on how to trace and debug AI coding agents with Omnigent and MLflow.
🎥
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2 weeks until Open Lakehouse + AI Meetup in Paris 🇫🇷
Building with MLflow? Don't miss @omnigent_ai: A Meta Harness for AI Agents. Aravind Segu and Edwin He will show how to capture @opentelemetry traces for observability in MLflow across multi-harness agent workflows.
🗓️ Sept 23 | 6–9:30 PM CEST
📍 La Fondation
👉 RSVP:
#MLflow# #AIAgents# #Paris#
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A workflow can move from Cursor to Claude Code to Pi to Codex by copy-pasting prompts and outputs. Each harness logs differently, which leaves a hole when you try to debug, audit, or trust the run.👇
This blog shows how @omnigent_ai unifies that interface and sends standardized traces to MLflow: agent turns, tool calls, per-turn tokens, and session metadata, with no app code changes.
🔗
#MLflow# #Omnigent#
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Jules Damji (@databricks) walks through what breaks when you switch coding harnesses: sharing context, carrying policies, and seeing whether the agents did the right thing. @omnigent_ai sits on top as a meta-harness; session-scoped traces go to MLflow.
📽️ Watch the tutorial:
#Omnigent# #MLflow# #OpenSource# @2twitme
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