See where real external agents break on your product.
Gauntlet tests whether real external agents can actually use your product from its docs, APIs, and tools.
Rate limit behavior undocumented in llms.txt. No retry guidance. Agent abandoned without recovery.
Trusted by agent-facing teams
You built for agents.
But can they actually use it?
Every agent-facing team assumes their product is reachable. Most are wrong in ways that only appear when real external agents try to adopt it from scratch.
Looks fine in demos
Your product works perfectly when you run it. You control the docs, the flow, and the inputs. External agents encounter a completely different surface.
Fails in the wild
Real agents call your API in unexpected order, misread ambiguous docs, skip auth steps, hit rate limits with no retry guidance, and abandon on first failure.
You find out too late
By the time a customer tells you their agent can't adopt your product, you've already lost the integration — and you still don't know exactly why it broke.
Where external-agent adoption breaks
Four steps from context to clarity
No setup overhead. Feed your docs, run the gauntlet, get a report that tells you exactly what to fix.
Ingest product context
Feed Gauntlet your llms.txt, llms-full.txt, OpenAPI specs, tool contracts, and any docs. It shapes this into a retrievable knowledge base.
Run agent workflows
Eight built-in agent personas run realistic workflows against your product surface — each with different behavior styles, knowledge slices, and failure vectors.
Capture traces and evidence
Every tool call, retry, decision, and failure is recorded as a structured trace. Nothing is inferred — everything is observed and logged.
Judge and report
Gauntlet attributes each failure to docs, product behavior, tooling, or model limits. You get a report with specific evidence and concrete fixes.
Concrete evidence, not just pass/fail
A completed run is not necessarily a good run. Gauntlet surfaces clean success, recovered success, suspect success, and hard failure — with full evidence for each.
Execution traces
Step-by-step records of every tool call, decision, and result for each agent run. Full fidelity, no summarization.
Failure attribution
Each failure is categorized: docs gap, product behavior, tooling issue, or model capability. Know which layer broke.
Reproduction paths
Exact steps to reproduce each failure. Give your team something concrete to fix, not a vague failure report.
Cross-persona analysis
See which agent types succeed and which fail — and why the same product produces different outcomes by persona.
Evidence-backed fixes
Recommendations anchored to specific trace evidence and doc citations. No guessing. No generic advice.
HTML + JSON reports
Human-readable dashboard for your team. Machine-readable JSON for your CI pipeline. Both generated automatically.
One command. One investigation bundle.
artifacts/gauntlet_runs/gauntlet_042/ · layer2_run_*.json · gauntlet_042.html · gauntlet_042.json
This is not an eval.
This is not observability.
Gauntlet occupies a category that didn't exist before: testing the product surface that agents consume, from the perspective of a real external agent trying to adopt it cold.
Test model quality and task capability
Test product quality and agent adoption readiness
Capture what happened in production
Find what will break before production, with layer-level attribution
Measure model performance on curated tasks
Measure whether your product surface is usable by real external agents
A human QA engineer covers ~20 scenarios
8 distinct agent personas run in parallel against your actual product surface
Eight agents, eight failure modes
These are not cosmetic variations. Each persona changes how the agent behaves, what docs it trusts, and what class of product failures become visible.
Follows docs literally
Happy path, baseline correctness
Hammers endpoints fast
Rate limits, concurrency
Reorders and perturbs flows
Edge cases, input validation
Misreads params, wrong endpoints
Doc clarity, error messages
Multi-step deep sessions
Timeouts, session continuity
Pushes boundaries and limits
Security, payload limits
Concurrent sub-tasks
State isolation, concurrency
Fails intentionally, then recovers
Idempotency, retry logic
Built for products that agents consume
If your product's end users are AI agents — or agent-mediated workflows — Gauntlet is built for you.
Browser & computer-use APIs
Products like browser automation APIs, headless infra, and session managers that agents call to take actions on the web.
Browserbase · Steel.dev · similar
Tool and integration platforms
MCP servers, action APIs, and integration layers that expose real-world tools to agents. If agents call your tools, they need to work reliably.
MCP providers · Composio-style · Nango
API and docs products
Any API product that wants external agents to adopt it from docs alone. Your llms.txt is your agent-facing contract — Gauntlet tests it.
API companies · docs-first products
Internal AI platform teams
Platform teams making their internal APIs consumable by agent-mediated workflows. Find the gaps before your internal agents hit them.
Enterprise AI infra · agent-mediated workflows
Common questions
Know if your product is actually agent-ready.
Get a diagnostic run against your API with a real, evidence-backed report. No sales cycle required to see the output.