Sample Agent Execution Assurance Report

Synthetic demonstration only. This sample is fictional and is not a client engagement, testimonial, certification, or representation of any real company's systems.

Scenario

A customer-service AI agent can review a refund request, retrieve account context, recommend an outcome, and trigger a refund workflow after approval.

Execution chain

Identity: Customer Support Agent v3 initiated the workflow under service identity svc_ai_refund.
Authority: The service identity had permission to read customer/order data and prepare refund actions up to a configured threshold.
Policy: Refunds above the threshold required a human approval before the payment system could be called.
Action: The agent retrieved the order, classified the request, calculated the proposed refund, and prepared the payment action.
Approval: A supervisor approved the recommendation through the support console.
State change: The payment processor recorded a refund and the order system changed the case to resolved.
Evidence: Request ID, policy evaluation, approval event, processor reference, and final state were expected to form one reconstructable chain.

Finding 1 — Approval evidence is not durably correlated

Observed condition: The support console records the approver and timestamp, but the approval event does not retain the same durable execution identifier used by the agent and payment action.

Why it matters: A reviewer can see that an approval occurred, but cannot prove that the approval authorized this exact execution without additional reconstruction work.

Priority: High

Remediation: Persist one immutable correlation identifier across agent execution, policy evaluation, approval, downstream action, and resulting state.

Finding 2 — Policy decision is logged as outcome, not evidence

Observed condition: The workflow stores approved/denied but not the evaluated rule version, threshold, inputs, or decision artifact.

Why it matters: The organization can see the result but cannot later reconstruct why the policy produced that result.

Priority: Medium-High

Remediation: Retain policy version, relevant decision inputs, rule result, timestamp, and evaluation identifier with the execution record.

Finding 3 — Resulting state is split across systems

Observed condition: Payment status and support-case status are updated independently and do not share an authoritative completion record.

Why it matters: A partial failure can create ambiguity about whether the financial action occurred, whether the customer was notified, and whether the case was actually complete.

Priority: Medium

Remediation: Create a final execution receipt that records expected effects, observed effects, processor reference, final business state, and reconciliation status.

Authority & approval matrix

Actor	Allowed action	Approval required?	Evidence expected
AI agent	Read context / recommend refund	No	Execution + policy trace
Supervisor	Approve refund above threshold	Yes	Approver identity + exact execution binding
Payment service	Execute refund	Only after approved state	Processor reference + resulting state

Decision memo

Recommended decision: Continue the bounded pilot only after the approval event and policy evaluation are tied to the same immutable execution identifier. Do not expand the workflow scope until the evidence chain is reconstructable across systems.