@henrytdowling: This is an amazing writeup on how you are probably not using your agent traces correctly
Summary
The article discusses common errors in using AI agent traces and offers advice on proper implementation.
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The tweet discusses how improving trace quality by showing sequential LLM and tool calls instead of nesting them makes debugging and enhancing AI agents easier.
Using an agent to understand agent traces
The article describes using Databricks Genie to query MLflow agent traces with natural language, enabling non-SQL users to analyze agent performance metrics like latency and errors.
Posted about the agent debugging spiral yesterday. The replies taught me more than my post did.
A developer reflects on community insights for debugging AI agents, emphasizing systemic reliability through techniques like logging tool calls and structured output validators.
@freeCodeCamp: AI agents can be hard to debug when all you see is the final output. In this tutorial, Darsh shows you how to trace and…
A tutorial by Darsh on how to trace and monitor local AI agents using LangSmith, LangChain, Ollama, and Qwen, enabling inspection of model and tool calls, latency, and usage.
@yoheinakajima: get the most out of your agent traces w @agnostai
Agnost AI launched its first model, agnost-*******-0.1, trained on production traces from a customer, achieving a 22.9% increase in task success and a 90.2% reduction in some metric.