Every Decision. Every Action. Fully Traced

Tamper-evident audit and replay across every prompt, tool call, and A2A hop

Once agents are discovered, observability gives you the continuous, immutable record of what they actually did — at the level of every prompt, every tool call, every intermediate decision, and every hop to another agent or MCP server. It's the layer that makes governance auditable and incidents recoverable.

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Standard logging was built for humans, not agents.

When a web request fails, you check the access log. When an agent fails — or does something it shouldn't — there often isn't a log to check. The agent's reasoning happened inside the model. Its tool calls used a service account. When the process ended, the audit trail ended with it.

Agents make decisions. They call tools. They pass data between systems. None of that is captured by the logging infrastructure enterprises built for human-driven applications. Observability for AI agents has to be built differently — at the protocol layer, not inside the agent itself.

Tamper-evident capture. At every hop.

Trust3 AI captures every agent action at the protocol layer — outside the agent process — so the record exists even when the agent doesn't. Every prompt, every tool call, every A2A handoff is logged with the originating user identity attached. The result is an audit trail that regulators can accept and incident responders can actually use.

Full-stack observability. At runtime.

Signal Capture

Every prompt. Every tool call. Every decision point.

Trust3 AI captures telemetry at the protocol layer — not inside the agent — so the record is complete regardless of how the agent was built or which framework it runs on. For each agent run, the following signals are captured:

Signal	What gets captured
Prompts	Every prompt the agent received — from the user, from another agent, from a retrieval pipeline, or from a tool response
Tool calls	Every MCP server or API the agent called, with arguments constructed and response received
Decisions	Intermediate reasoning steps where the agent chose between tools, paths, or actions
A2A hops	Every agent-to-agent handoff — which agent called which, with what identity, carrying what payload
Data accesses	Which datasets, tables, or files were reached, under which user identity, with which policy decision applied
System state	Agent version, model version, tool catalog at time of call — required for incident reproduction

Identity Propagation

The part most teams get wrong. Every hop carries the real identity.

The standard pattern for connecting agents to MCP servers and downstream APIs is a long-lived service account. That means every downstream log says the service account did it. The user who initiated the chain is invisible. Least-privilege becomes impossible because the real principal is never in the request.

Trust3 AI propagates the originating user identity and a purpose claim through every hop of the chain — A2A, MCP, and direct data calls. Downstream systems enforce policy against the real principal. Audit logs attribute actions to the human who initiated the chain, not the robot that executed it.

Originating user identity carried through every A2A and MCP hop
Purpose claim attached at initiation and verified at each step
Service account activity attributed back to the triggering human
Up to three hops of A2A identity depth — covering the topologies most enterprises run today
Compatible with Okta, Entra ID, and custom identity providers

Tamper-Evident Audit Trail

An audit trail regulators can accept. Not just logs developers can read.

Standard agent logs live inside the agent process and disappear when the process ends. A compromised agent can rewrite its own logs. Neither property is acceptable for regulated environments.

Trust3 AI writes telemetry to an append-only, tamper-evident store outside the agent process. The record is complete before the agent finishes. It cannot be modified after the fact. It survives agent failure, restart, or compromise.

Append-only capture — no post-hoc modification
Captured outside the agent process — survives agent failure
Cryptographic integrity — tamper-evident by design
Configurable retention aligned to your audit horizon and regulatory requirements
Exportable to SIEM, data lake, or compliance tooling

Incident Replay

Reconstruct any agent run. End to end, from the record alone.

The right test of observability is whether an analyst can reconstruct what an agent did — from first prompt to final action — without interviewing the engineer who built it.

Given any flagged event, Trust3 AI lets you answer:

Which user initiated the chain, and what was their original request?
What did the agent retrieve, and what did it decide based on that retrieval?
Which MCP server was called, and what did it return?
Where in the run did the unexpected action happen?
What data was exposed, to what principal, under which policy?

Every question is answered from the telemetry record, not from inference. This is what EU AI Act Article 12 logging requirements are written to require for high-risk systems.

SIEM & SOC Integration

Agent telemetry in the tools your security team already uses.

Observability is most valuable when it flows into the tools your security operations team already runs detections and playbooks against. Trust3 AI routes agent telemetry as structured, normalized events into your existing stack.

Target	How Trust3 AI connects
SIEM	Structured agent events with identity normalized to originating user. Detections against agent behavior look like any other workload once identity is correct.
SOAR	Pre-built playbooks for agent-specific incidents: kill-switch invocation, prompt injection detection, unusual tool-call sequence.
Data lake	Long-retention agent activity for trend analysis, drift detection, and audit horizon coverage.
Compliance reporting	Periodic exports mapped directly to control evidence for SOC 2, ISO 27001, and EU AI Act reporting cycles.

Behavioral Drift Detection

Catch scope creep before it becomes an incident.

Agents rarely exceed their authority in a single dramatic move. They drift. New tools get added. The prompt chain gets longer. A new MCP server gets connected. A new data source gets reachable. Each change is small. Aggregated over months, a narrow-scope agent becomes something much broader.

Trust3 AI compares current agent behavior against its established baseline continuously. Drift surfaces as a change in the observability stream — new tools invoked, new data reached, new A2A peers, longer reasoning chains. Each is flagged for review before it becomes a policy violation.

Baseline established automatically at deployment
Continuous comparison against current behavior
Alerts on new tool invocations, data sources, A2A peers, or prompt patterns
Drift triggers re-attestation workflow, not just a notification

Need answers fast? Ask in plain English.

Trust3 returns answers grounded in your live telemetry, with links to the full trace records. No custom queries, no waiting on the engineering team.

"What did the finance copilot do between 2pm and 4pm yesterday?"
"Which agents accessed PII data in the last 7 days?"
"Show every A2A hop that crossed a trust boundary this week."