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Agentic systems improve through a disciplined loop: observe real traces, find the mistakes that matter, and apply the right fix. Sovara keeps that loop grounded in recorded behavior instead of guesswork. The workflow is intentionally simple. Traces show the trace, annotations focus review, and lessons turn recurring domain knowledge into runtime context.
Sovara workflow from observing traces to surfacing errors and improving agents
1

Observe

Start from real executions. Sovara keeps the trace steps and runtime context together so the team can inspect the behavior that actually occurred.The concrete observability views are covered in Traces, Manual inspection, and Sovara chat.
2

Surface errors

Not every trace deserves manual review. Sovara prioritizes the failures, regressions, confusing outputs, and behavioral gaps most likely to teach the team something.Review surfaced traces in Annotations. To learn how Sovara chooses which traces deserve attention, read about our Recommendation Algorithm.
3

Improve

Once you understand the error, apply the right kind of fix. Some issues need a better prompt or a new tool. Others come from missing domain knowledge, where a reusable lesson is the better fix.To understand that pattern, start with What are lessons?. Then learn how to create and manage lessons, and how Sovara performs auto-injection at runtime.