SprintiQ™Agents aren't trustworthy. They're auditable.
A structured engagement that produces the evidence record your deployed AI agents are missing. STRIDE threat modeling, FMEA risk scoring, and NIST AI RMF maturity mapping, in a single audit artifact your OIG examiner, general counsel, or board can defend.
"The model said so" is not an administrative record.
Your agents are running. They're making determinations. None of that survives an OIG inquiry, a GAO review, or a congressional oversight letter.
What's missing isn't capability. It's the audit trail, decision provenance, and interpretable reasoning chain that turn agent output into defensible action. Most agentic systems treat auditability as a logging problem, not a design constraint, and produce outputs without the evidence structure accountability requires.
The agents making consequential determinations today will face oversight inquiries within two to three years. Organizations that built the governance layer will have answers. The ones that didn't will improvise under pressure, on compressed timelines, with elevated political visibility.
A structured diagnostic. Not a monitoring tool.
The Agent Trust Audit is a fixed-scope engagement that walks your deployed or staging agent system through three integrated frameworks: STRIDE for threat surface, FMEA for risk prioritization, and NIST AI RMF for maturity scoring against the de facto federal standard.
One coherent artifact, traceable from threat to mapped control to prioritized remediation. Built to land in three audiences without rewriting: technical teams who need a remediation backlog, compliance leadership who need a NIST-mapped scorecard, and executive or oversight readers who need a one-page verdict and three priorities.
Not vendor software with a dashboard. A senior practitioner engagement with chain-of-custody hashing and methodology version pinning, so the work is reproducible and defensible.
The audit runs on Argus, a knowledge engine with four layers working together: a threat library of known AI attack patterns, a regulatory library of federal compliance standards, an incident library of real-world AI failures, and an architecture library that learns from every audit Argus performs. It draws on all four to ground each finding in established evidence, and sharpens with every engagement. The result is an audit you can defend: every finding traces to a real source, not an AI's opinion, which is exactly what holds up when regulators, procurement officers, and auditors come asking.
Three frameworks.
One coherent risk signal.
The methodology stack is what separates an audit from a checklist. Each framework solves a problem the others cannot.
STRIDE
Six threat dimensions adapted to agentic systems: Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, Elevation of Privilege. Applied per node and per edge of your agent graph, including intermediate actions, not just endpoints. Repudiation is where most government deployments fail first: the inability to reconstruct, after the fact, what the agent did and why.
FMEA
Failure Mode and Effects Analysis. Each threat is scored on Severity, Probability, and Detectability, producing a Risk Priority Number. Detectability is scored at the intermediate-action level, the blind spot of most monitoring dashboards. The RPN drives remediation sequencing.
NIST AI RMF
The de facto federal compliance reference. Four functions, Govern, Map, Measure, and Manage, each subcategory scored at one of four maturity levels (nothing, asserted, documented control, tested control) with evidence attached. A baseline mapped to the framework your OIG examiner, ATO reviewer, or compliance committee already recognizes.
One audit signal, propagating end to end. STRIDE identifies the threat. FMEA scores it. NIST locates it on a maturity scale your stakeholders already speak.
Seven phases.
One audit artifact.
Each phase produces structured outputs that feed the next: nothing is reentered, nothing is reasoned about twice.
A defensible record,
in the formats your stakeholders use.
The deliverable bundle is built to circulate without reformatting. Every artifact is hash-anchored and methodology-pinned for reproducibility.
Built for environments
where the audit is real.
The audience for this work is people personally on the hook when something goes wrong.
Federal & state government
Agencies with deployed or pre-deployment agent systems making consequential determinations: benefit eligibility, program matching, fraud detection, regulatory triage. OIG audit readiness, NIST AI RMF Critical Infrastructure Profile alignment, and procurement language compliance are the active drivers.
Healthcare
Provider organizations, payors, and digital health platforms deploying agents in clinical decision support, prior authorization, care coordination, and revenue cycle. FDA Software as a Medical Device guidance and HHS AI assurance expectations are reshaping what counts as a defensible deployment.
Supply chain & logistics
Operations using agentic systems for sourcing, routing, demand forecasting, and supplier risk. Regulatory exposure varies by sector, but the audit value is the same: a documented risk register and a sequenced remediation path before an incident creates the audit by force.
Regulated enterprise
Financial services, energy, and any organization where a deployed agent has produced a determination a regulator, plaintiff, or board could legitimately ask to reconstruct.
SprintiQ Inc. is a certified Service-Disabled Veteran-Owned Small Business. Dr. Nagy served on active duty in the US Army as a Combat Medic and is a fourth-generation combat veteran. The firm is eligible for SDVOSB set-aside contracting.
Built for the audit,
not the demo.
If you have agents in production or staging and haven't run a structured trust audit, start before the next deployment milestone, not after. The first conversation is a scoping call. You get a written engagement scope within five business days.