Agentops

Observability for AI Agents in Production

AgentOps is an AI agent observability platform designed specifically for monitoring, evaluating, and debugging AI agent workflows in production environments. As autonomous AI agents become more prevalent in production systems, the need for visibility into what agents are doing, why they are making decisions, and where they are failing becomes critical. AgentOps addresses this monitoring gap.

Agent-Specific Observability

Unlike general application monitoring tools, AgentOps understands the structure of AI agent workflows — sequences of LLM calls, tool uses, memory operations, and decision points. It captures and visualizes the full execution trace of agent runs, making it possible to replay and analyze exactly what happened during any agent session. Cost tracking per agent run, error detection, and performance benchmarking are all built around the agent execution model.

• Full agent execution trace recording and replay
• LLM call and tool use monitoring with latency metrics
• Cost tracking per agent run and across deployments
• Error detection and alerting for agent failures
• Integration with LangChain, AutoGen, CrewAI, and other frameworks

For AI Engineering Teams

Engineering teams deploying AI agents in production use AgentOps to maintain visibility into agent behavior, debug failures quickly, and ensure that agent performance meets quality standards. The platform integrates with popular agent frameworks through simple SDK instrumentation, minimizing the effort required to add observability to existing agents.