AgentOps

About AgentOps

AgentOps is an observability and monitoring platform built specifically for AI agent applications. It gives developers visibility into how their agents behave at runtime, tracking things like LLM calls, tool usage, session flows, and costs across multi-agent pipelines. A core feature is session replay, which allows developers to reconstruct and review exactly what happened during an agent run, making it significantly easier to debug unexpected behavior, trace errors back to their source, and understand decision paths that would otherwise be opaque. The platform is designed for developers building production-grade AI agent systems, particularly those working with frameworks like LangChain, AutoGen, CrewAI, and similar orchestration tools. AgentOps integrates with these ecosystems to capture telemetry without requiring extensive instrumentation from scratch. Use cases include debugging failed agent runs, monitoring token consumption and associated costs, evaluating agent performance over time, and ensuring reliability before and after deployment. Teams shipping autonomous or semi-autonomous agents to end users benefit most, since those systems tend to involve long, branching execution chains where a single misconfigured tool call or hallucination can cascade into larger failures. AgentOps positions itself as infrastructure for the emerging category of agentic AI, filling a gap that general-purpose APM tools like Datadog or New Relic were not built to address. Rather than treating an agent session as a simple API call, it models the full lifecycle of an agent run as a structured, replayable event. The platform appears to offer a free tier for developers getting started, with pricing scaling based on usage volume for teams and enterprises. It is a practical tool for any engineering team that has moved beyond prototype-stage agents and needs real accountability and diagnostic capability in their production environment.

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