The assurance control plane for the enterprise AI stack.
Quality assurance for AI systems that plan, act, and decide. AI Range tests the whole agentic system, continuously, and produces the per-run Evidence Pack a regulator will accept.
No one tests the whole agentic system.
Traditional AI evaluations test a model. Security tools inspect selected vulnerabilities. Observability platforms show runtime signals. Governance tools organize policies. Agentic failures live in the seams between them.
Every connector, every autonomous step, every MCP tool call widens the four A's: Access, Autonomy, Action, and Accountability. The real risk lives in the connections between everything else.
The wider the surface, the less you can see.
Security gap
Undetected vulnerabilities reach production. Remediation happens after the incident, and reactive remediation costs billions.
Observability gap
AI behavior is unexplainable in production. Deployments stall, approvals drag, and first-mover advantage evaporates.
Governance gap
Policies exist on paper. When a regulator asks for proof the governed system actually behaves, no artifact answers the question.
Agentic risk lives in the seams, and those seams are where traditional testing stops.
A harness around the entire system.
AI Range wraps the whole AI system in a continuous test harness. Adversarial scenarios go in. Scored, verified, signed evidence comes out.
Attack it the way reality will.
Ten concurrent executors run adversarial scenarios against the live system: agents, tools, retrieval, and memory together, not a model in isolation.
Score it against controls.
Every run is evaluated across five dimensions, then verified against 21 policy controls in 7 families. Pass and fail are machine-checked, not asserted.
Walk away with the artifact.
Each run produces a signed Evidence Pack with a SHA-256 chain of custody, mapped to NIST AI RMF, OWASP LLM Top 10, and MITRE ATLAS.
Six stages. One regulator-ready outcome.
The full TEVV pipeline runs per system, per production cycle. Context governs test selection, so no two deployments are evaluated the same.
Discover
9-section intake. Readiness Score 0–100.
Map
Topology graph. MITRE ATLAS classification.
Test
Executor Swarm. MICE adversarial scenarios.
Evaluate
Risk Severity, Likelihood, and 5-dimension scorecard.
Verify
OPA Rego. 21 controls, 7 families.
Validate
Evidence Pack PDF + JSON. SHA-256.
A signed, per-run compliance artifact with a SHA-256 chain of custody. It records what the system was asked, what it did, what passed, what failed, and which controls verified it. This is what an examiner sees.
See the breach before it happens.
Guardrails do not fail all at once. They erode, turn by turn and release by release. Peregrine is designed to track behavioral trajectory in production and predict guardrail breach before it occurs.
Designed to apply kinematic motion math to guardrail integrity: not whether a breach happened, but how fast one is approaching.
Designed to produce a vendor-neutral mathematical fragility fingerprint for any model in the agent portfolio.
Built to quantify resilience under sustained adversarial pressure, run over run, release over release.
AI Range proves readiness before deployment. Peregrine is built to watch behavior after it. Together: continuous assurance from pre-production through production.
Explore Peregrine
Your AI writes code. Prove it follows the rules.
Engineering teams are deploying agents that write code, review pull requests, triage incidents, and gate releases. They touch private repos, customer data, and CI/CD pipelines. Four questions decide whether they reach production:
Permission Boundary
Can the agent reach only approved code, tickets, docs, and tools? Repo boundaries, role-scoped tool calls, PII exposure.
Policy Compliance
Does it follow SDLC, security, privacy, and approval rules, even under adversarial pressure?
Workflow Reliability
Can it complete multi-step work without skipping gates, dropping handoffs, or drifting out of scope?
Production Monitoring
Can failures, drift, guardrail erosion, and risky actions be observed and evidenced after go-live?
AI Range answers all four with evidence, across 8 canonical use cases spanning the agentic software development life cycle (SDLC): from code generation copilots to multi-agent workflows.
In April 2026, federal banking regulators replaced SR 11-7 with a risk-based framework that puts agentic AI in scope by principle. Examiners will ask how you know your agents are safe. The Evidence Pack is the answer.