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Agentic systems improve through a disciplined loop: observe real runs, 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 run, annotations focus review, and priors turn recurring domain lessons into runtime context.
Sovara workflow from observing traces to surfacing errors and improving agents
1

Observe

Start from real executions. Sovara keeps the run graph and runtime context together so the team can inspect the behavior that actually occurred.The concrete observability views are covered in Runs, Manual inspection, and SovaraChat.
2

Surface errors

Not every run deserves manual review. Sovara prioritizes the failures, regressions, confusing outputs, and behavioral gaps most likely to teach the team something.Review surfaced runs in Annotations. To learn how Sovara chooses which runs 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 prior is the better fix.To understand that pattern, start with What are priors?. Then learn how to create and manage priors, and how Sovara performs auto-injection at runtime.