Watch @MLflow capture a full trace of one agent request: the chain-of-thought reasoning, every tool call, the timing of each span, on the OpenShift AI dashboard. Here's @LegareKerrison on seeing what your agent actually did, not just what it answered:

Traditional monitoring tells you an API responded successfully in five seconds. It can't tell you your agent called the wrong tool, or fed the model the wrong context. @LegareKerrison breaks down AI observability with @MLflow: tracing, LLM-judge evaluation, and OpenTelemetry for multi-agent systems.

Your AI assistant says 44% pull-through, 60 days to close. The dashboard says 43.2% and 52.5. Did the model hallucinate? Trace it with @MLflow and you see the agent got 44% and 60 straight from the tool. It summarized correctly. The bug is upstream: the tool and the dashboard just disagree. That's the shift, from "the AI is wrong" to a bug someone can actually fix. @cedricclyburn and @LegareKerrison on how AI observability works:

[DEMO] Define features once, then use the same definitions through training and production. See how Databricks Feature Store helps teams build batch and streaming features with Feature Views, create training sets with automatic point-in-time joins, and productionize those features with built-in governance and observability. Plus, see how Genie Code and MLflow support iteration along the way.

GPU utilization increased from ~20% to 43% on a reservation of 96 NVIDIA H100 GPUs, while cutting queue starvation by 74%, with blocked jobs falling from roughly 10 per day to around 4. That’s the concrete result @SpreeAI saw after fixing their orchestration. When a unified diffusion model requires 80–100 GB of memory, you can’t simply throw workloads at a cluster and expect to use those GPUs efficiently. SPREEAI was dealing with workload fragmentation, ad-hoc submissions, and storage I/O blocking that left expensive GPUs idle. Working with Lambda’s ML engineering team, they implemented MLflow-based experiment orchestration with structured queuing and workload matching. They also connected Lambda’s Prometheus APIs to Grafana for real-time visibility into utilization gaps. The video testimonial covers how they diagnosed the bottlenecks and what the remediation looked like.

📣 Hey Paris: Join us on Sept 23 for the next Open Lakehouse + AI meetup! Aravind Segu and Edwin He will cover why to use a meta-harness: collaborate on your work, exercise control, and choose your coding agent harnesses. The talk includes a demo of Omnigent’s workflow, including @opentelemetry traces in tools like @MLflow. 🗓️ Wed, Sept 23 | 6:00–9:30 PM GMT+2 📍 La Fondation, Paris 🎟️ Register: #Paris# #Omnigent# #OpenSource#