The context: a refund automation agent, mid-market retail SaaS
The agent being audited handles customer refund requests autonomously. It reads support tickets, classifies the request, and issues refunds directly against the payment system. It passed standard internal evaluation before the audit. The audit found four gaps that would have caused production incidents within days of live deployment.
Overall trust score: 61/100 — not production-ready. The agent has genuine capability. The governance layer is absent.
Governance — selected findings
Examines authority scope, approval gates, and audit trail. This is the dimension most often passed over because the agent “works” — it performs the task. The governance question is what happens when it performs the task incorrectly at scale, and whether there is a recovery path.
The agent has authority to issue refunds up to $AMOUNT without any human confirmation step. When a customer’s request matches the classification criteria, the refund executes directly against PAYMENT-SYSTEM with no preview or dry-run path. A single misfired classification on a bulk support ticket batch would initiate multiple refunds before detection. No rollback path is documented or tested.
The agent logs requests and outcomes to LOG-SYSTEM, but the log format omits decision rationale (which classification criteria triggered the refund) and the full customer context that was evaluated. Reconstructing what the agent “saw” at decision time is not possible from current logs. 6 of 8 test scenarios produced incomplete logs.
Hallucination risk — selected finding
Examines whether the agent’s outputs are grounded in its actual knowledge, whether it fabricates references, and whether it has a defined escalation path when confidence is low. For a customer-facing support agent, hallucinated policy citations are a direct liability.
In 2 of 12 test prompts involving edge-case refund policy queries, the agent cited policy clause references that do not exist in the provided policy documentation. Both hallucinated citations were plausible in structure. A customer who received this response would have no reason to doubt it. Both hallucinations were detected only because the evaluator had the policy document open for comparison.
Data sensitivity — the most urgent finding
Examines PII handling, data minimisation, and session isolation. For agents that process customer data, this is often the dimension where a single finding blocks deployment entirely.
Full customer PII — including DATA-FIELDS — is passed verbatim to the LLM prompt context. No masking, tokenisation, or data-minimisation layer exists between the CRM integration and the LLM invocation. This was confirmed by network capture of 3 test agent invocations. Critically, this finding is compounded by the injection vulnerability found in the Agent Surface section: a successful prompt injection that causes the agent to echo its context would expose PII in the output.
Agent attack surface — the highest-exploitability finding
Examines whether the agent can be manipulated by the untrusted data it processes in normal operation. For agents that ingest customer-submitted text, this is not a theoretical risk. The attack surface is publicly accessible.
Customer support ticket bodies are passed into the agent’s context without sanitisation. An attacker who submits a crafted support ticket can embed instructions that redirect the agent’s behaviour. 3 of 5 injection test scenarios successfully redirected agent behaviour. Payload patterns that succeeded: role-play override, fictional context injection, and instruction-appending via Unicode formatting characters. Any customer who can submit a support ticket can attempt this. No credentials or prior access are required.
Gap register — top 5 of 9 (excerpt)
The full report includes a ranked gap register with all 9 findings sorted by severity and exploitability. Below are the top 5.
| # | ID | Finding | Category | Severity |
|---|---|---|---|---|
| 1 | D-1 | PII in LLM context without masking | Data Sensitivity | Critical |
| 2 | AS-1 | Indirect prompt injection via ticket body | Agent Surface | Critical |
| 3 | G-1 | No approval gate before refund issuance | Governance | Critical |
| 4 | G-2 | Audit trail insufficient for compliance review | Governance | High |
| 5 | G-3 | Authority scope exceeds documented mandate | Governance | High |
Production readiness checklist — excerpt
The third deliverable is a concrete, agent-specific checklist your team can answer with yes or no. This excerpt shows five of nine items from the sample audit.
- Agent authority is scoped to minimum necessary permissions. Current state: agent has broad order-history read access beyond the active order scope.
- An approval gate or dry-run path exists before consequential actions execute. Current state: no gate between classify and execute for refund issuance.
- PII is masked or tokenised before LLM context inclusion. Current state: full PII passed verbatim in prompt payload.
- Tool access is authenticated via a service credential. Pass — service account is present.
- Rollback path is documented and tested for consequential actions. Current state: no rollback procedure exists for refunds issued in error.
Overall checklist result: 2/9 pass. See all 9 items in the full report →
What this report is — and what it is not
The findings in this sample are real patterns from a real production agent review. Client identity, system names, thresholds, data field names, and regulatory references are redacted.
The report is not a generic checklist applied mechanically. Every finding names the specific evidence that led to it. Every remediation is scoped to the agent’s actual design — not a template answer. The production readiness checklist references specific workflows and integration points, so your team can carry it forward as an ongoing governance baseline after the engagement ends.
The $799 AUD fixed price covers five business days of review, all three deliverables, and a 7-day written follow-up window. See the full offer details →
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