Use with AI · Chapter Application

Audit Whether This Agent Verifies Success Outside the LLM

Apply Agents From First Principles Step 09 to find places where an agent trusts its own narration instead of authoritative external evidence.

Chapter Application Agents From First Principles Agent verification boundaries Intermediate
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How to use this

  1. Open a repository-aware AI assistant.
  2. Give it access to the repository or files you want reviewed.
  3. Copy the prompt below and run it unchanged first.
  4. Use the evidence it finds to decide what to inspect or change next.
PromptCopy and run against your own project
You are auditing an AI agent repository for false-success risk.

The central question is:
Where can the agent say "done" without an independent system establishing that the intended postcondition is actually true?

Do not treat model confidence, self-critique, a successful tool call, or a plausible final answer as verification.

Inspect the repository in this order:

1. Identify completion paths.
   Find every place a task/run can enter a terminal success state.
   Record:
   - file and symbol
   - success status/value emitted
   - evidence required before that transition

2. Reconstruct the task contract.
   For each major task type identify:
   - intended external outcome
   - authoritative system of record
   - observable postcondition that would prove success

3. Classify current evidence.
   For every success path classify the strongest evidence available as one of:
   - model assertion
   - model critique/judge
   - tool response
   - deterministic local check
   - authoritative external read
   - independent verifier

4. Find false-PASS paths.
   Look for cases where:
   - the model marks its own work complete
   - a tool returning HTTP/command success is treated as business success
   - generated code is accepted without tests/static checks/runtime evidence
   - browser actions are assumed to have taken effect without rereading state
   - writes are considered successful without checking the authoritative resource
   - UNKNOWN/ambiguous outcomes are collapsed into PASS

5. Check verifier independence.
   Determine whether the verifier:
   - observes the result independently
   - has access to authoritative state
   - can return PASS / FAIL / UNKNOWN
   - is insulated from persuasive generator rationale where practical

6. Check failure handling.
   What happens when verification is unavailable, contradictory, stale or ambiguous?
   Flag any fail-open behavior that converts missing evidence into success.

7. Propose the minimum verification boundary.
   For each high-value false-success risk, specify:
   - exact postcondition
   - authoritative evidence source
   - deterministic check where possible
   - when an LLM-based semantic verifier is genuinely needed

Return:

A. Completion-state map
B. Confirmed false-PASS risks
C. Weak or non-independent verification
D. Missing authoritative postconditions
E. Places where UNKNOWN must remain UNKNOWN
F. Smallest verification changes to implement first
G. Tests that prove the agent can no longer claim success incorrectly

Governing rule:
The agent may produce the action, but it does not get to define reality.