Observability

Guides on tracing, monitoring, debugging, cost, latency, and production behaviour across AI agent workflows.

Observability

Product

AI Agent Observability Best Practices: A Step-by-Step Guide

Learn how AI agent observability helps teams trace LLM calls, tool use, retrievals, latency, token usage, cost, and failures across production agent workflows.

Netra MCP Explained: How to Debug AI Agents Without Leaving Your IDE

Product

Best Practices

Observability

Netra MCP Explained: How to Debug AI Agents Without Leaving Your IDE

Debugging AI agents usually means bouncing between your IDE, dashboards, and logs to piece together what went wrong. Netra MCP brings that observability context directly into your coding assistant, letting you query live traces and pinpoint failures without ever leaving your editor.

Shape how your agent behaves

Know it works before it ships

Find what can break it

Catch problems as they happen

Improve with every run

Observability

AI Agent Observability Best Practices: A Step-by-Step Guide

Netra MCP Explained: How to Debug AI Agents Without Leaving Your IDE