What Agent Scrimmage Tests
2663+ scenarios across 17 industries. Every evaluation covers discovery, simulation, scoring, and fix generation.
Discovery Engine
Before running a single scenario, we probe your agent to map exactly what it can and can't do. Discovery extracts capabilities, guardrails, limitations, and tool access in about 30 seconds. For skill-file agents, we analyze the files directly — extracting persona rules, workflow steps, boundary definitions, and domain knowledge. For API agents, we send diagnostic prompts and analyze the response patterns. The result is a capability profile that tells you precisely what your agent claims to do, what it actually does, and where the gaps are.
Scenario Simulations
2663+ realistic scenarios across 17 industries. Each scenario is grounded in real-world data — actual compliance regulations, real industry metrics, documented failure patterns. An angry customer demanding a refund while threatening a lawsuit. A user trying to trick your agent into revealing internal policies. A vendor sending fake bank details for a $28,000 wire transfer. Every scenario includes specific success criteria, failure indicators, and a ground-truth response summary so scoring is objective, not subjective.
Mock Infrastructure
For agents built in Claude Code or similar environments, we simulate the production infrastructure your agent normally has access to. File systems with realistic project structures. Persistent memory across conversation turns. CRM connections with real customer records, open invoices, and support tickets. API endpoints that return realistic responses. Your agent can demonstrate its full workflow — reading files, updating records, querying databases — without touching your production systems. We provide mock data packs for Salesforce, HubSpot, Shopify, and more.
Training Asset Generation
We don't just find problems — we generate the fixes. After evaluation, you get structured training assets: updated skill files with corrected guardrails, routing rules that prevent the failures we found, I/O schemas defining expected inputs and outputs, and example conversation pairs showing the correct response for every failed scenario. Download as a ZIP, plug directly into your agent's configuration, and re-evaluate to verify the fixes worked. Most agents improve 15-30 points on re-evaluation.
Most agents improve 15-30 points after applying generated training assets
Self-Growing Scenario Library
Our scenario library isn't static. When you connect an agent, we analyze its capabilities and automatically generate custom scenarios targeting its specific domain. A GTM audit agent gets pipeline coverage scenarios. A dental receptionist bot gets appointment scheduling edge cases. A construction project manager gets change order disputes with real markup calculations. The scenarios are grounded in industry research — we scrape regulatory data, Reddit pain points, and real AI agent workflows to ensure every test reflects what actually happens in production.
Real-Time Scoring
Watch your agent pass or fail in real time. Every response is scored on four dimensions: conversation quality (40%), tool usage accuracy (20%), output quality (20%), and diagnostic accuracy (20%). The overall readiness score tells you at a glance whether your agent is ready for production. Scores below 70 mean critical issues that will affect customers. Scores above 90 mean your agent handles edge cases, stays honest about limitations, and maintains professional tone under pressure. The scoring rubric is transparent — you see exactly why each score was given.