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.

What core capabilities does AgentOps provide?

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.

How does AgentOps lifecycle work?

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.

What kind of monitoring and analytics does AgentOps offer?

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.

What AI agents and frameworks does AgentOps support?

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.

Is AgentOps suitable for enterprise use?

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

How to get started with AgentOps?

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.

Can AgentOps be self-hosted?

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

What are best practices for rollout and safety?

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.

What distinguishes AgentOps from general LLMOps?

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.

AgentOps

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AgentOps helps developers build, test, and run reliable AI agents and LLM apps. It offers clear visibility into agent actions, simplifying debugging, cost tracking, and performance assurance. You can monitor agent behavior, replay sessions, and manage deployments efficiently across over 400 AI frameworks.

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Overview Features Use Cases FAQ Integrations Pricing

About AgentOps

Who It's For

AgentOps helps developers create and run AI agents or large language model (LLM) apps. If you need reliable agents that stay within budget and are easy to fix, this tool is for you. It's for managing AI systems from testing to live use.

What You Get

You see everything your AI agents do. This includes tracking AI model calls and tool actions. AgentOps helps you watch spending on tokens and debug issues by replaying agent runs. You also get tools for agent safety and proper management.

How It Works

Just add a small software kit (SDK) to your agent's code. This SDK connects with over 400 AI frameworks. AgentOps then automatically records all agent activities. Use a dashboard to watch, debug, and check your agent's performance and costs live.

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Features & Capabilities

⚙️ Agent Observability & Debugging

Real-time Visualization

Visually tracks events such as LLM calls, tool usage, and multi-agent interactions.

Time Travel Debugging

Allows replaying and rewinding agent runs with precise point-in-time control for debugging.

Comprehensive Debugging & Auditing

Maintains a complete data trail of logs, errors, and detects prompt injection attacks from development to production.

Agent Agnostic SDK

Provides a single SDK for observability across OpenAI, CrewAI, Autogen, and over 400 LLMs and frameworks.

💰 Cost Management & Optimization

Granular Token Tracking

Monitors, saves, and tracks every token processed by your AI agent.

Real-time Cost Tracking

Manages and visualizes agent spending with up-to-date price monitoring across multiple agents.

LLM Fine-tuning Optimization

Enables fine-tuning of specialized LLMs using saved completions for up to 25x cost savings.

🔒 Enterprise & Compliance

Unlimited Log Retention & Events

Offers unlimited storage for agent events and comprehensive log retention.

Advanced Security & Compliance

Provides features like custom SSO, SOC-2, HIPAA, and NIST AI RMF compliance for secure operations.

Flexible Deployment Options

Supports self-hosting on major cloud platforms (AWS, GCP, Azure) and on-premise deployments.

Role-based Permissioning

Manages user access and permissions within the platform with role-based controls.

Use Cases

Debugging Autonomous AI Agents in Development

AI engineers often struggle to diagnose unexpected behaviors or errors in complex autonomous agents that involve multiple LLM calls and tool uses. AgentOps provides comprehensive observability, session replay, and time-travel debugging capabilities, allowing developers to visually trace every step of an agent's execution, pinpoint root causes, and iterate faster.

AI/ML DevelopmentFor: AI Engineers

Managing and Optimizing AI Agent Operational Costs

Companies deploying AI agents face challenges in tracking and controlling the unpredictable costs associated with LLM usage and token consumption. AgentOps provides granular cost tracking, token usage monitoring, and spend visualization across all agents, enabling teams to manage budgets, identify areas for optimization, and even fine-tune LLMs on historical data for improved efficiency and reduced costs.

B2B SaaSFor: Engineering Managers

Ensuring Production Reliability and Enterprise Governance for AI Agents

Enterprises struggle with maintaining the reliability, safety, and compliance of AI agents once deployed into production, especially concerning data handling, performance, and ethical considerations. AgentOps provides an enterprise-grade control plane with real-time monitoring, policy enforcement for data scope and high-risk actions, and robust deployment strategies, guaranteeing agents operate reliably, securely, and within defined service-level objectives.

Financial ServicesFor: AI/ML Ops Teams

Accelerating Iterative AI Agent Development and Evaluation

Developing effective AI agents involves continuous iteration, testing, and evaluation, which can be time-consuming without proper tools to understand agent performance and behavior. AgentOps streamlines the development lifecycle by providing comprehensive evaluation tools, including session replays, benchmarking, and detailed event logging, enabling developers to quickly test hypotheses, identify improvements, and rapidly iterate on agent designs.

Software DevelopmentFor: Product Developers

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