AgentOps AI

Developer platform for testing, monitoring, and debugging AI agents in production.

4.3 (6)

리뷰어 Daniel Nikulshyn·업데이트됨 2026년 5월

Observability Analytics SDK LLM Agents Testing Developer Tools Monitoring

개요

AgentOps AI is an observability and evaluation platform built for teams shipping LLM-powered agents. It captures detailed traces of agent runs, including tool calls, prompts, costs, and errors, so developers can understand exactly what their agents did and why. The platform pairs runtime monitoring with testing and simulation features, letting teams replay sessions, benchmark changes, and catch regressions before they reach users. With SDKs for popular agent frameworks, it integrates into existing workflows with minimal setup.

주요 기능

Session replay and trace analytics
Agent testing and simulation
Cost and token tracking
SDK integrations with agent frameworks
Error and regression detection
Production monitoring dashboards

사용 사례

Debug Agent Failures in Production

Replay agent sessions and inspect traces of tool calls, prompts, and errors to pinpoint why an agent misbehaved and fix issues quickly.

Catch Regressions Before Deployment

Run simulations and benchmark changes against previous agent versions to detect regressions before pushing updates to users.

Track LLM Costs and Token Usage

Monitor token consumption and spending across agent runs to optimize prompts, control costs, and forecast budgets accurately.

Monitor Live Agent Performance

Use production dashboards to track agent health, error rates, and behavior in real time across deployed LLM-powered applications.

장단점

장점

Detailed session replay and trace visibility
Works with major agent frameworks
Combines monitoring with evaluation tooling
Helps track cost and token usage

단점

Geared toward developers, not non-technical users
Requires instrumentation of agent code
Most useful for teams already running agents in production

리뷰

4.3

6개 평가의 평균.

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Margaret Whitfield

Does the job

Pretty happy overall. Cost and token tracking just works and detailed session replay and trace visibility. Geared toward developers, not non-technical users can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Diego Fernández

Does the job

Pretty happy overall. Error and regression detection just works and works with major agent frameworks. Geared toward developers, not non-technical users can be annoying, but no dealbreakers — I'd recommend it to a friend without hesitating.

Sofia Lindqvist

Compared a few options

Evaluated this against two competitors. Where it wins: session replay and trace analytics and combines monitoring with evaluation tooling. Where it lags: geared toward developers, not non-technical users. On balance the feature set — especially session replay and trace analytics — justifies the 4 stars for our use case.

Rina Desai

Years in this space

I've evaluated a lot of these over the years. What stands out here is agent testing and simulation — handled better than most — and detailed session replay and trace visibility. Worth the time if this is your use case.

Camille Laurent

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on error and regression detection, and detailed session replay and trace visibility caught me off guard. still, I'd recommend giving it a real trial.

Omar Haddad

Skeptical, then convinced

I went in skeptical — most tools in this space overpromise. It actually delivers on production monitoring dashboards, and works with major agent frameworks caught me off guard. Requires instrumentation of agent code is why this isn't a perfect score, still, I'd recommend giving it a real trial.