Synthetic acceptance testing
Swarms: send AI users through your MCP server before real ones arrive.
Swarms are AI agents that act like your users. You define personas; a swarm runs multi-turn journeys through your MCP server across every client and surfaces where it fails the job, continuously and before launch.
Acceptance testing at agent scale
Personas you define
Describe the kinds of users your server serves, and the swarm runs as those personas, not a single scripted path.
Multi-turn journeys
Each agent runs realistic, multi-turn journeys through your server, the way a real user would, and surfaces where it fails the job.
Across every client, continuously
Swarms run across the clients your server will meet, continuously and before launch, instead of once in a manual pass.
Why synthetic acceptance testing
Executing one successful tool call proves the server works today. It doesn't prove it holds up when many different users, with many different intents, exercise it across clients. Manual acceptance testing can't cover that surface, and waiting for real users means finding the failures in production.
Swarms close that gap: AI personas run the journeys your users would, at a scale a human QA team can't match, so the failure modes show up before launch instead of in a bug report.
Frequently asked questions
Swarms are AI agents that act like your users. You define personas; a swarm runs multi-turn journeys through your server across every client and surfaces where it fails the job, continuously and before launch.
Evals score specific test cases against your server's tools. Swarms run open-ended, persona-driven journeys at scale to find where the server fails real user goals. It's acceptance testing rather than case-by-case scoring.
Swarms are a paid feature for teams. The open-source core covers local inspection, protocol validation, and evals.
Find the failures before your users do.
See how Swarms tests your MCP server with AI personas at scale.