Observability

If your agents can reach more valuable futures with fewer steps, they’re stronger—whether you measured that task or not. Today’s paper offers a clean way to turn that intuition into a number: empowerment—an information‑theoretic score of how much an agent’s current action shapes its future states. The authors introduce EELMA, a scalable estimator that works purely from multi‑turn text traces. No bespoke benchmark design. No reward hacking. Just trajectories. This is the kind of metric we’ve wanted at Cognaptus: goal‑agnostic, scalable, and diagnostic. Below, I translate EELMA into an operator’s playbook: what it is, why it matters for business automation, how to wire it into your stack, and where it can mislead you if unmanaged. ...

TL;DR Most teams still analyze pipelines with brittle SQL, custom scripts, and static dashboards. A new reference architecture shows how schema-driven LLM agents can read workflow provenance in real time—across edge, cloud, and HPC—answering “what/when/who/how” questions, plotting quick diagnostics, and flagging anomalies. The surprising finding: guideline-driven prompting (not just bigger context) is the single highest‑ROI upgrade. Why this matters (for operators, data leads, and CTOs) When production AI/data workflows sprawl across services (queues, training jobs, GPUs, file systems), the real telemetry isn’t in your app logs; it’s in the provenance—the metadata of tasks, inputs/outputs, scheduling, and resource usage. Turning that exhaust into live answers is how you: ...

When a network fails, it doesn’t whisper its problems—it screams in silence. Packet drops, congestion, and flapping links rarely announce themselves clearly. Engineers must piece together clues scattered across logs, dashboards, and telemetry. It’s a detective game where the evidence hides behind obscure port counters and real-time topological chaos. Now imagine handing this job to a Large Language Model. That’s the bold challenge taken up by researchers in “Towards a Playground to Democratize Experimentation and Benchmarking of AI Agents for Network Troubleshooting”. They don’t just propose letting LLMs debug networks—they build an entire sandbox where AI agents can learn, act, and be judged on their troubleshooting skills. It’s not theory. It’s a working proof-of-concept. ...