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The Sazabi agent does the querying and correlating; your job is to point it at the right problem and read what it produces. This page covers the artifacts it renders, how to steer a long investigation, how project memory shapes what you should ask, and how to reference the objects in your project.

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Artifacts the agent produces

The agent does not only reply in text. When it helps, it renders artifacts inline in the thread:
  • Charts. Time-series visualizations of what it found — for example error rate over the last hour.
  • Tables. Structured results, such as the top failing endpoints with counts.
  • Code. Snippets and diffs, including references to the exact file and line when your code is connected.
  • Pages. Longer formatted write-ups the agent assembles to explain what happened.
Every artifact is grounded in the queries the agent ran, and you can expand the tool calls to see the underlying data.

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Steering long investigations

A good investigation is a conversation, not a single prompt.
  • Start with the symptom and a time window (“checkout errors are up since 14:00 UTC — what changed?”).
  • Read the agent’s tool calls as it works; if it is looking in the wrong place, say so and it will adjust.
  • Ask follow-up questions in the same thread so the agent keeps the context it has built.
  • If a thread has gone down the wrong path, fork it from an earlier message and take a different angle.

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How project memory shapes what you ask

The agent draws on project memory — durable notes and runbooks your team has curated. When memory holds the context (which service owns checkout, how you define “healthy”), you can ask higher-level questions and skip the setup. When it does not, give the agent that context in your prompt, and consider writing it to memory so the next investigation starts ahead.

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Referencing issues, components, and log streams

Refer to your project’s objects by name or ID in your prompt, and the agent looks them up as part of its investigation — for example “investigate issue about failed webhook deliveries” or “show me logs from the checkout service.” The agent resolves these against your project’s data rather than requiring a special syntax.

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Further reading

Use project memory

Give the agent durable context.