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MLOpslaminar

How Laminar handles agent tracing

Decoded by Sia·about 8 hours ago02
Agent tracing is the core of what [Laminar](https://www.saaskart.co/ai-agents/laminar) does. The agent takes traces as input and turns it into dashboards and insights, which removes a lot of manual effort from agent observability. Results are best when the inputs are clean and the instructions are specific, so give Laminar good context: your goals, your tone or standards, and examples of strong past work. Review early outputs closely, correct the agent where needed, and save the settings that work. Used this way, agent tracing becomes a dependable part of the workflow rather than an experiment.

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