AgentOps

AgentOps is a developer platform and set of practices designed to manage the lifecycle of autonomous AI agents. It enables teams to build, test, monitor, debug, deploy, and continuously improve AI agents (including those based on over 400 large language model (LLM) frameworks) with enterprise-grade safety, observability, and governance.

AgentOps provides a control plane for AI agents to manage their operation in production, ensuring safety, efficiency, and transparency. It also offers observability to trace every agent action and tool call with timing, success/error codes, token usage, costs, latency, and stability metrics. Safety and governance features allow defining enforceable policies for data scope, refusal conditions, and approval workflows, and setting service-level objectives (SLOs) like latency and token budgets with alerting for drift. Furthermore, continuous evaluation is included, with session replays, benchmarking, and detailed event and interaction logging for debugging and performance tracking.

The AgentOps lifecycle involves planning measurable outcomes and policies, then building and evaluating agents using testing and debugging tools. Next, it requires applying safety controls, service-level objectives (SLOs), and enforcing policies. Finally, it executes safe rollout strategies such as shadow mode and canary release with rollback mechanisms.

AgentOps offers real-time monitoring of LLM calls, tool usage, agent interactions, latency, costs, and failures. It provides session drill-down views to analyze event types, recurring patterns ("repeat thoughts"), and operational behaviors of agents. Additionally, a replay capability allows users to rewind and inspect agent runs for debugging.

AgentOps supports flexible use with over 400 LLM frameworks including OpenAI, CrewAI, Autogen, LangChain, and watsonx, providing SDKs (notably Python) for easy integration and extended instrumentation.

Yes, it includes enterprise-grade governance, control plane features, and observability designed for scale and reliability in production environments.

To get started with AgentOps, sign up at AgentOps.ai and obtain an API key. Then, install the agentops Python package via pip, and initialize it with your API key to automatically begin tracking agent data. Finally, use the AgentOps dashboard to monitor and analyze agents in real time.

Yes, AgentOps provides documentation and guides for self-hosting the backend services to run infrastructure independently if desired.

Best practices for rollout and safety include using shadow mode on live traffic first, followed by a phased canary roll out while monitoring against baselines. It is also important to set frozen rollback and alert mechanisms for any performance or safety issues, and to precisely scope tool access and require approvals for high-risk actions.

While LLMOps focuses on managing and optimizing single large language models, AgentOps oversees the entire lifecycle of AI agents involving complex reasoning, external interaction, multi-agent workflows, and decision-making in dynamic environments.