AI Agent Observability: Why Did It Do That?

Recording an agent’s full execution trajectory — sessions, turns, model calls and tool calls, with arguments, results, tokens, cost and latency — so you can answer why it behaved as it did. It differs from application monitoring in the unit of observation: a request has a start and an end, while a trajectory is a nested sequence in which any level can loop.

LLM observability is a subset covering the model call: prompt, completion, tokens, latency, cost. Agent observability adds everything around it — which tools ran, in what order, with what arguments and results, and how many times a turn went around before stopping. Most real failures live in that structure, not in a single completion.

Not if model and tool traffic already routes through a gateway, since it records each hop as a side effect of serving it. Framework-level SDK instrumentation adds internal reasoning steps the gateway never sees, so the two are complementary, not alternatives.

Yes — VPC, on-prem, air-gapped, hybrid, or across multiple clouds, with no data leaving your domain.

Yes. It includes an MCP Gateway, an Agent Gateway, and an MCP & Agents Registry with tool-level access control. Agents on LangGraph, CrewAI, AutoGen, or a custom framework can all be governed centrally.

Oui. La passerelle est compatible OpenTelemetry et s'intègre à Grafana, Datadog, Prometheus, ou à votre pile technologique préférée. Elle trace chaque requête, du prompt à l'exécution de l'outil et du modèle, vous offrant ainsi une journalisation unifiée sans avoir à remplacer ce que vous utilisez déjà.