Author(s): Rittika Jindal

Originally published on Towards AI.

This is Part 4 of a 4-part series: “The Honest Guide to Building AI Agents That Actually Work.”

In Part 1, we fixed context loss. In Part 2, we designed tools agents can actually use. In Part 3, we gave agents episodic memory.

The article explores the critical topic of observability in AI agents, discussing how traditional logging fails to provide insights into an agent’s decision-making process. It introduces Langfuse, an open-source tool designed to enhance visibility into the functioning of AI agents. With features like tracing and evaluative metrics, Langfuse allows users to monitor agent performance, identify failures, and optimize processes. The piece emphasizes the necessity for observability to resolve issues effectively, shares practical setups for using Langfuse, and reinforces that the key to improving AI systems is visibility into their operations.

Read the full blog for free on Medium.

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Published via Towards AI