AgentOps is an observability platform for tracing, debugging, replaying, and monitoring AI agents and LLM application workflows.

Who should use AgentOps?

It is best for developers and AI teams building production agents with tools, model calls, multi-agent steps, or framework integrations.

What does AgentOps help with?

It helps teams inspect agent runs, replay sessions, track costs, monitor errors, audit behavior, and improve reliability over time.

How is AgentOps priced?

The official site lists a free Basic plan up to 5,000 events, Pro starting at $40 per month, and custom Enterprise pricing.

What makes AgentOps different?

It focuses on agent-specific observability with replay analytics, LLM cost tracking, framework integrations, and deployment support.

What should buyers watch for?

Buyers should confirm event limits, retention, export needs, framework support, security requirements, and self-hosting terms.

AgentOps

Name: AgentOps
Brand: AgentOps

Observability and analytics platform for debugging, testing, and monitoring AI agents.

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What is AgentOps?

TL;DR

AgentOps is an observability and analytics platform for AI agents that helps teams trace runs, inspect tool calls, replay sessions, monitor costs, and debug autonomous workflows in development and production.

Track and debug AI agent runs across tools and steps
Replay sessions and inspect agent decisions
Monitor costs, latency, errors, and behavior over time

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About

AgentOps is an observability and analytics platform for developers building AI agents. It helps teams track agent runs, inspect tool calls, debug failures, replay sessions, monitor costs, and understand how autonomous or semi-autonomous agents behave in development and production. The product is useful for SaaS founders and technical teams building agents with frameworks such as CrewAI, AutoGen, LangChain, or custom orchestration. Agents can be hard to debug because they take multiple steps, use tools, call models, and make decisions that are not always visible. AgentOps provides a layer for tracing and analyzing those behaviors. Teams can use AgentOps to monitor reliability, catch errors, improve prompts, understand bottlenecks, and compare agent changes over time. This is especially useful for support agents, research agents, workflow automation, coding agents, data agents, and AI products that need more than a single LLM call. For FutureStack, AgentOps belongs in Developer Tools because agent observability is becoming a core part of production AI infrastructure. It is best for developers and founders who are building agents and need to debug, monitor, and improve their behavior before scaling to real users.

Use Cases

Agent Observability

Trace multi-step agent runs, tool calls, model interactions, and decisions so developers can understand production behavior.

Session Replay

Replay agent sessions to reproduce failures and investigate what happened across complex multi-step workflows.

Agent Cost Tracking

Monitor model and workflow costs across agent runs to understand spending and optimize production workloads.

Framework Monitoring

Instrument agents built with popular frameworks and custom orchestration so teams can monitor reliability over time.

Key Features

Track and debug AI agent runs across tools and steps
Replay sessions and inspect agent decisions
Monitor costs, latency, errors, and behavior over time
Integrates with agent frameworks and production workflows

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