Test an AI Agent’s Permissions and Escalation Boundaries

• RA

  Relevance AI

  AI Automation & Agents

  Platform for building AI agents and tools, billed in Actions and Vendor Credits

  Paid◦Documented

1. 1

   Step 1: Inventory tools, data and side effects

   List each available tool, credential scope, readable data, writable data, external message and irreversible effect. Include indirect effects such as a draft later consumed by another automation. Identify owners and environments. Do not assume a read-labelled tool is harmless if it can expose sensitive data. The inventory must match actual configuration before production testing.

2. 2

   Step 2: Define allow, deny and escalate policy

   Create a matrix with action, resource, condition, permitted role, approval requirement and refusal behavior. Deny by default when a combination is not listed. Separate the ability to propose an action from the ability to execute it. Define escalation for uncertainty, sensitive data, financial impact and conflicting instructions. Policy text should be understandable to a human reviewer without model-specific jargon.

3. 3

   Step 3: Configure instructions and approvals

   Use the bounded-instructions and escalation prompts to implement the policy. Restrict integrations and credentials at the platform level as well as in natural-language instructions. Put approvals immediately before side effects and include the exact proposed payload. Consult current Relevance AI security and trigger documentation because capabilities and controls may vary by configuration.

   Tool: Relevance AI

4. 4

   Step 4: Build an adversarial test suite

   Create cases for legitimate allowed tasks, denied requests, ambiguous authority, prompt injection, encoded instructions, data exfiltration, tool chaining and approval bypass. Define the expected decision and permitted tool calls before execution. Include failures in dependent tools and stale approval. Use synthetic data and an isolated environment. The suite should test controls, not reward a polite refusal message.

5. 5

   Step 5: Run tests and inspect actual tool behavior

   Capture input, model response, attempted and completed tool calls, approval interruptions, data accessed and final state. Compare each with the matrix. Treat any prohibited attempted call as a failure even if a downstream service rejected it. Distinguish platform enforcement from model compliance. Record false blocks too, because an unusably restrictive agent can cause operators to bypass controls.

6. 6

   Step 6: Tighten controls, rerun and document limits

   Fix failures at the strongest available layer: remove tool access, narrow credentials, validate inputs, add deterministic policy checks or insert approval. Rerun the complete suite to detect regressions. Document unresolved risks, untested integrations and monitoring. Expansion to new tools or data invalidates part of the evidence and requires a fresh review.

• Visit Relevance AI ↗

• Draft bounded agent instructions · tested on OpenAI GPT-5 (Codex) — editorial dry run
• Add permission and escalation boundaries · tested on OpenAI GPT-5 (Codex) — editorial dry run
• Generate adversarial agent tests · tested on OpenAI GPT-5 (Codex) — editorial dry run

• OutputIllustrative scenario: permission matrix rerun

  Synthetic fixture: 18 cases. The first policy allowed one approval-bypass proposal and over-blocked one read-only lookup. Revised controls moved account changes behind approval, retained refund denial and restored the bounded lookup. All outcomes remained simulated.

• DatasetTesting limitation

  The agent, tools, logs and approvals were synthetic and evaluated in Codex on 2026-10-03. Relevance AI, credentials and live customer systems were not used.

✓Last verified Oct 3, 2026