AI Agent Governance

Agent Trust Stack (ATS)

A layered trust model for AI agents operating in high-stakes and regulated environments. As AI agents move into clinical, financial, and regulated operational contexts, the question is not capability - it is trust architecture.

The Internet Had SSL. Agents Have Nothing.

When the internet needed trust, we built cryptographic protocols. When AI agents need trust, most companies are building... nothing. ATS maps technical safeguards to human decision authority at each level of autonomy.

Three Layers of Trust

ATS defines what your AI agent can decide alone, what it must escalate, and what it can never do - enforced by architecture, not policy.

Layer 1

A - Auditability

Can every agent decision be reconstructed, explained, and defended to a regulator?

Decision Reconstruction

Every action your agent takes must be traceable back to the inputs, reasoning chain, and outputs. Not just logs - a complete audit trail that survives regulatory review.

Explainability Requirements

Different regulatory contexts require different levels of explanation. ATS maps your agent's decisions to the explainability standard required by your regulators.

Evidence Generation

Audit trails designed to produce evidence, not just operational data. When a regulator asks "why did the agent do this?", you have a defensible answer.

The Test:
If the FDA asks why your agent recommended a treatment path, can you reconstruct the exact reasoning from six months ago?

Layer 2

T - Trust Boundaries

Explicit architecture defining what the agent decides alone versus what it escalates to a human.

Autonomy Mapping

A clear, documented map of which decisions your agent can make independently and which require human approval. Not policies - structural enforcement.

Escalation Architecture

When the agent encounters a decision outside its trust boundary, what happens? ATS defines the escalation paths, timeouts, and fallbacks.

Boundary Enforcement

Trust boundaries enforced by code, not by guidelines. The agent literally cannot make certain decisions without human intervention - not "shouldn't," but "can't."

The Test:
Is there any way for your agent to make a high-stakes decision without a human in the loop? If yes, is that intentional?

Layer 3

S - Safeguards

Structural prevention of irreversible harm. Architecture that physically cannot allow certain failure modes to propagate.

Harm Prevention Architecture

Certain actions should be impossible, not just discouraged. ATS identifies the irreversible harm scenarios for your context and builds structural prevention.

Fail-Safe Defaults

When something goes wrong, what state does the system fail to? ATS ensures your agent fails safe - defaulting to human control, not autonomous action.

Propagation Limits

If the agent makes a mistake, how far can that mistake propagate before it's caught? ATS designs blast radius limits into the architecture.

The Test:
What's the worst thing your agent could do if it went completely wrong? Is that outcome structurally impossible?

Applied to Your Environment

ATS is applied to client environments to map current trust architecture and identify the highest-risk gaps before they surface in production. Every AI Governance engagement includes an ATS assessment.

Assess Your Agent Trust Architecture

Ready to build AI agents that regulators trust?

Every AI Governance engagement includes implementation of the Agent Trust Stack. Let's map your current trust architecture and identify the gaps.