Testing and evaluating AI agents
Tools for testing, evaluation, tracing and observability show whether an AI agent does what it should – before and while it operates.
AI agents do not behave deterministically like classic software. The same task can lead to different solution paths. Testing individual functions is therefore not enough: you evaluate entire workflows, the quality of answers and the decisions made along the way.
The tools in this category cover different tasks – from automated evaluations (evals) and step-by-step tracing to dashboards for ongoing operation.
Role in an AI-first business model
Implemented, tested, rolled out and economically effective are four different things. Evaluation makes the difference visible: it shows whether an agent meets the agreed quality criteria before it is given more room to act.
In a business model where you pay for results rather than activity, this is no formality. Only by continuously measuring quality and impact can you develop agents responsibly and detect problems early.
What to look for when choosing
- Evaluation of entire workflows (trajectories), not just individual answers
- Traceable logs of tool calls and intermediate steps
- Test data that reflects real business situations
- A link to the metrics by which business success is measured
8 tools in this category
Voice Lab
A framework for testing and evaluating voice agents. It helps you compare different language models, prompts and agent personas in a systematic way.
Open-RAG-Eval
An open-source framework for evaluating RAG systems that works without predefined reference answers. It can also be used to assess RAG tools connected to an AI agent (agentic RAG).
EvoAgentX
A framework that automatically evaluates agentic workflows and evolves them step by step. The project aims at an ecosystem of self-improving AI agents.
Arize-Phoenix
An open-source library for testing, evaluation and observability of AI agents. It helps teams understand agent behaviour and assess its quality.
Manifest
A self-hosted, open-source platform for real-time cost observability of AI agents. A local dashboard tracks tokens, costs, messages and model usage, with data ingested via OTLP.
agent-qa
An agentic QA harness for web and mobile tests written in natural language. It uses memory to adapt to UI changes and aims to catch regressions before a release ships.
Latitude
An open-source observability and evaluation platform for AI agents built on OpenTelemetry. It traces agent runs, groups production failures into issues and derives evals from them; it can be self-hosted.
ClawMetry
An open-source, real-time observability dashboard for agent runtimes such as Claude Code, Codex or Cursor. It shows sessions plus token and cost analytics and accepts data via OTLP.
From tool to outcome
In AI-first coaching you clarify which outcomes AI agents should be accountable for in your go-to-market and within which framework. REVAGO then designs, builds and runs your system. You pay for results – not for activity.
Key concepts
Other categories
A neutral overview, not a recommendation of individual providers. REVAGO is not affiliated with the listed projects. Descriptions are based on the projects’ public information; features and licences may change. Selection based on awesome-agents (kyrolabs), As of 19 October 2026.