Agent Ops

AgentOps is a developer platform for building AI agents and LLM apps with better observability. It records what your agent did during a run, so you can trace LLM calls, tool usage, and multi-agent interactions when something goes wrong.

It’s for engineers and teams shipping agentic systems in OpenAI, CrewAI, Autogen, or other LLM frameworks where you need an audit trail and cost visibility. If you’re dealing with unreliable behavior, hard-to-reproduce bugs, or you want to control spend across multiple agents, AgentOps focuses on the data you need after the fact.

• Session replay to review what happened in an agent run (LLM, tools, and actions)
• Time travel debugging to rewind and replay runs with precise point-in-time detail
• Debug and audit logs covering errors and prompt injection attack attempts from prototype to production
• Agent-agnostic SDK used across agent frameworks
• LLM and token tracking (400+ models/frameworks) so you can see token counts and token spend your agent consumed
• Cost tracking across multiple agents to visualize spend by model and by agent activity
• Replay analytics to analyze event histories instead of guessing

What sets it apart is that it treats debugging and reliability as an event trail problem. Instead of only surfacing logs, it keeps enough run context to replay and audit interactions, including security-relevant signals like prompt injection attempts.