We start by documenting and visualizing how the agent operates:

• Connect via Model Context Protocol (MCP), SDK, or documentation upload
• Automatically map tools, permissions, decision flows, and escalation paths
• Identify control points like approvals, filters, and human-in-the-loop steps

Output:
An Agent Architecture Dossier — the foundation for all evaluations.

Every agent is pressure-tested against foundational risk categories:

• Security & Privacy: injection resistance, tool misuse, sensitive data leakage
• Reliability: safe failures, retry behavior, infinite loops, reproducibility
• Auditability: traceability, versioning, evidence capture

Output:
A Baseline Risk Score with severity-rated findings and evidence-linked traces.

This is where domain depth matters.

• Apply domain-specific scenarios that mirror real workflows
• Grade behavior using expert-designed tests
• Produce clear explanations for what passed, what failed, and why

Output:
A Domain Knowledge Score tied directly to job functions and decision quality.

Findings aren’t meant to sit in a report.

• Each issue includes guidance pointing to the failure mode
• You fix prompts, policies, orchestration, or constraints
• We rerun the same scenarios to confirm improvements hold

Output:
Before-and-after evidence proving what changed — and that it sticks.

Everything is packaged into an artifact that stakeholders can rely on without rebuilding the analysis internally.

• Clear methodology and coverage
• Evidence-linked findings and traces
• Mapping to SR 11-7 and NIST AI RMF (where relevant)

Output:
An Independent Agent Assurance Report plus a digital Evidence Locker.

Agents change. Prompts evolve. Tools update.
So agentic assurance shouldn’t be one-and-done.
Run a critical subset of Universal Tests and SMEvals™ on a schedule:

• Monitor drift and regression
• Detect emerging failure modes
• Get alerts before issues reach customers or regulators

Stay deployable as production changes.