Checkpoint generates the test suite for your agent — happy paths, edge cases, adversarial prompts, policy boundaries — runs it in a synthetic environment, and scores every trace with an LLM judge. Find every failure before a single customer does.
Your team writes the cases they can imagine. The failures live in the cases they can't.
Real failures happen across multi-turn tool calls. A static prompt list can't surface them.
By the time a customer hits a policy edge case, the trust hit has already happened.
Paste a system prompt and tool schema. Get a structured suite covering the five failure modes that actually break agents in production. Edit any case. Add your own. Version it like code.
Run the suite in a generated environment with mocked tools, stateful side effects, and replayable traces. No staging cluster. No customer data. No surprises in production.
Every test has a structured rubric. The judge model scores each dimension and surfaces a verdict you can read, version, and trust. Trace-level explanations on every failure — no more "it just felt wrong."
Paste your system prompt and tool schema. Or point Checkpoint at an OpenAPI / MCP spec.
Get a structured suite covering happy paths, edge cases, adversarial prompts, policy boundaries, and ambiguous inputs. Review and edit any case.
Run against your live agent or in the synthetic environment. Get rubric-scored results back, with judge reasoning on every fail.
Currently onboarding our first beta cohort. Drop your email and we'll reach out within 48 hours.