saga-observability-platform

You can monitor AI agent reasoning quality by tracking inference trajectories, visualizing decision paths, and applying streaming hallucination detection to identify confidence drops and deviations in real time.

Agent observability is the tracking of agent actions and reasoning steps. Trace visualization works by creating visual timelines of the agent decision-making process to pinpoint where errors occur.

To trace decision paths when an agent output seems incorrect, review the entire reasoning trace to pinpoint where the error occurred and understand the confidence levels throughout the process.

Yes, you can build dashboards that provide real-time and historical views of agent performance metrics to facilitate monitoring and detect inference anomalies as they occur.

Traceability for agent monitoring focuses on reviewing reasoning trajectories and detecting anomalies, but it requires existing agent outputs to analyze and does not prevent errors before they happen.