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Observability#

AG2 OpenTelemetry Tracing: Full Observability for Multi-Agent Systems

AG2 OpenTelemetry Tracing

Multi-agent systems are powerful -- but when something goes wrong, figuring out where and why is painful. Which agent made the bad decision? Was the LLM call slow, or did the tool fail? How many tokens did that group chat actually use?

AG2 now has built-in OpenTelemetry tracing that gives you full visibility into your multi-agent workflows. Every conversation, agent turn, LLM call, tool execution, and speaker selection is captured as a structured span -- connected by a shared trace ID and exportable to any OpenTelemetry-compatible backend.

Key highlights:

  • Four simple API functions to instrument agents, LLM calls, group chats, and A2A servers
  • Hierarchical traces that mirror how agents process conversations
  • Distributed tracing across services using W3C Trace Context propagation
  • Works with any backend -- Jaeger, Grafana Tempo, Datadog, Honeycomb, Langfuse, and more
  • Follows OpenTelemetry GenAI Semantic Conventions for standard interoperability

AG2 Event Logging: Standardized Observability with Python Logging

AG2 Event Logging

AG2 now integrates with Python's standard logging module for event output, giving you full control over how agent events are captured, formatted, and processed. This integration brings enterprise-grade observability directly into your agent workflows.

This article explores how to configure and customize AG2 event logging, with practical examples for testing, monitoring, and production deployments.

AgentOps, the Best Tool for AutoGen Agent Observability

AgentOps and AutoGen

TL;DR

  • AutoGen® offers detailed multi-agent observability with AgentOps.
  • AgentOps offers the best experience for developers building with AutoGen in just two lines of code.
  • Enterprises can now trust AutoGen in production with detailed monitoring and logging from AgentOps.

AutoGen is excited to announce an integration with AgentOps, the industry leader in agent observability and compliance. Back in February, Bloomberg declared 2024 the year of AI Agents. And it's true! We've seen AI transform from simplistic chatbots to autonomously making decisions and completing tasks on a user's behalf.

However, as with most new technologies, companies and engineering teams can be slow to develop processes and best practices. One part of the agent workflow we're betting on is the importance of observability. Letting your agents run wild might work for a hobby project, but if you're building enterprise-grade agents for production, it's crucial to understand where your agents are succeeding and failing. Observability isn't just an option; it's a requirement.

As agents evolve into even more powerful and complex tools, you should view them increasingly as tools designed to augment your team's capabilities. Agents will take on more prominent roles and responsibilities, take action, and provide immense value. However, this means you must monitor your agents the same way a good manager maintains visibility over their personnel. AgentOps offers developers observability for debugging and detecting failures. It provides the tools to monitor all the key metrics your agents use in one easy-to-read dashboard. Monitoring is more than just a “nice to have”; it's a critical component for any team looking to build and scale AI agents.