AgentOps
AgentOps is an observability and monitoring platform purpose-built for AI agents, providing session replays, cost tracking, and debugging tools for multi-step, tool-using LLM agent workflows. It helps developers understand and troubleshoot…
Definition
AgentOps is an observability and monitoring platform purpose-built for AI agents, providing session replays, cost tracking, and debugging tools for multi-step, tool-using LLM agent workflows. It helps developers understand and troubleshoot agent behavior across frameworks like LangChain, CrewAI, and AutoGen.
Overview
As LLM applications evolved from single-turn prompts into autonomous, multi-step agents that call tools, browse the web, and make decisions across many LLM calls, traditional application monitoring proved insufficient for understanding what agents actually did and why they failed. AgentOps was built to address this gap specifically for agentic workloads, providing session-level visibility into an agent's full execution trace — every LLM call, tool invocation, and intermediate decision — rather than just isolated API request logs. The platform integrates with popular agent frameworks (including LangChain, CrewAI, AutoGen, and others) via lightweight SDK instrumentation, capturing structured traces of agent runs that can be replayed step by step in a dashboard. This lets developers pinpoint exactly where an agent went wrong — a bad tool call, a hallucinated intermediate step, an infinite loop — which is often much harder to diagnose in multi-agent systems than in single-shot LLM calls. Beyond debugging, AgentOps tracks cost and token usage per session and per agent, helping teams manage the often unpredictable spend associated with agentic workflows that may make dozens of LLM calls to complete one task. It also supports monitoring for safety and compliance concerns, such as flagging when an agent attempts an unexpected or risky action. AgentOps competes in the growing LLM observability space alongside tools like Helicone, Braintrust, and Arize AI, but differentiates by focusing specifically on the session-and-trace model needed for multi-agent and tool-using systems rather than general single-call LLM logging.
Key Features
- Full session replay of multi-step agent executions
- Framework integrations for LangChain, CrewAI, AutoGen, and others
- Per-session and per-agent cost and token usage tracking
- Step-by-step trace visualization for debugging tool calls and decisions
- Alerting for anomalous or risky agent behavior
- Lightweight SDK-based instrumentation requiring minimal code changes
- Dashboards for comparing agent performance across runs and versions