Documentation Index

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FAQs
Glossary

    FAQs

      • How can I Track Token Usage and Cost Across Different LLM Models?
      • How can I Monitor Complex AI Agent Workflows in Production?
      • How do I Monitor Tool Calls in My AI Agents?
      • Is Maxim AI Compatible with OpenTelemetry (OTel)?
      • What is Agent Observability and Why do I Need It?
      • Can I Get Alerted for Any Regressions In Cost, Latency, or Any Other Evaluation Metrics?
      • How can I Observe and Evaluate Multi-Turn Trajectories with Maxim AI?
      • How can I Collect And Add User Feedback To A Trace?
      • How can I Evaluate Voice Agents with Realistic Voice Simulations?
      • How can I Integrate Maxim to Trace and Evaluate My Voice Agents?
      • What are Traces and Spans in Agent Observability?
      • How can I Curate Datasets From My Production Logs?
      • Can I Track Evaluation Costs and Token Usage at the Eval and Repository Levels?
    Observability

    How can I Observe and Evaluate Multi-Turn Trajectories with Maxim AI?

    Maxim lets you observe and evaluate multi-turn agent behavior using Sessions, which represent end-to-end task executions.

    Each session groups together all traces generated across multiple turns, giving you a complete view of how context evolves as the agent plans, reasons, performs actions, and responds over time. This makes it easy to inspect the full trajectory rather than fragmented, single-turn logs.

    On top of sessions, you can attach evaluators such as task success, trajectory quality, or custom agent metrics to measure their real-world performance. These evaluations can be monitored over time and used to detect regressions, unexpected behaviors, or quality drops in production.

    Can I Get Alerted for Any Regressions In Cost, Latency, or Any Other Evaluation Metrics?
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