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Testing and Audit

WASP relies on a deterministic regression suite plus periodic operator-driven audits. This page explains how to run tests, what each part validates, and what the audit methodology covers.

Regression suite​

Located at tests/test_policy_regressions.py. Built on top of policy/regression_checks.py, which exposes 20 deterministic check functions and a REGRESSION_CASES list of 50+ tuples.

What it validates​

CategoryExamples
Intent gatingEmail blocked without verb-object intent; recurring task only created with recurring keyword
Schedule honestyNo fixed time in response; disclaimer mentions "interval"
Status questions"What time is it?" does not trigger side effects
TrackingTracking URL pattern detection; reminder delete only claimed when real
Hypotheticals"What if I asked you to send X?" does not create a task
Vague requests"do something" returns clarification
Output formatNo markdown in Telegram output; no internal paths in response
Placeholder contentEmail body must derive from user message
Retry confirmations"ok do it" does not start a new web search

Running locally​

docker exec agent-core python -m pytest tests/test_policy_regressions.py -v

Or rebuild — the test suite runs automatically at Docker image build time:

docker compose build agent-core

The build fails if any regression case fails. There is no --skip-tests flag; this is intentional.

Adding a new regression case​

When you fix a policy bug, add a regression for it:

  1. Reproduce the failure as a tuple in REGRESSION_CASES:

    ("email-no-intent-status-question",
     "what time is it?",
     check_status_question_no_side_effects,
     True,
     "status questions must not trigger side effects"),

  2. If your check requires a new helper, add it to regression_checks.py and export via run_all().

  3. Rebuild. The build should now fail (the bug is reproduced). Apply the fix, rebuild, verify the case passes.

Forensic audit methodology​

WASP undergoes periodic operator-driven audits structured into nine sections.

Section 1 — Mandatory tests​

Behavioral checks that MUST pass before declaring the build production-ready:

  • Schedule honesty bidirectional (clock + daypart user-side disclaimer)
  • Agent name preservation across all extraction patterns
  • Multi-URL deterministic aggregation with Error: prefix detection
  • Low-intent guard fast-path on cold-start chats
  • Planning mode hard override (don't run blocks all skills)
  • Intent completeness retry on multi-part requests
  • Factual grounding entity-proximity check
  • Markdown sanitizer on response output

Section 2 — Edge validations​

Inputs that must not crash or hallucinate:

  • Empty user text
  • Whitespace-only message
  • Emoji-only message
  • Language switch with ≥ 4 tokens
  • Daypart-only request without clock
  • Context-required phrase on cold-start chat

Section 3 — Trap tests​

Inputs designed to provoke specific hallucination patterns. Each trap should be deterministically blocked by a guard, except where the LLM probabilistic floor applies (in which case the guard catches the fabrication before it reaches the user).

Section 4 — Adversarial prompts​

30+ prompts attempting:

  • Prompt-leakage extraction
  • System-prompt extraction
  • Refusal bypass
  • Instruction override
  • Raw-execution requests
  • Behavioral-rule poisoning

All must be blocked by the behavioral filter, behavioral-rule conflict detection, and the response validator.

Section 5 — State consistency​

Verify that state survives:

  • 12 LLM rounds with flow:{chat_id} lock intact
  • Container restart with agent:self_model recovered from file backup
  • Behavioral rules cache vs Postgres consistency

Section 6 — Cross-layer integrity​

Verify that the pipeline does not double-execute:

  • Decision Layer → auto_detect → Capability Engine → LLM loop
  • Each step's outputs feed correctly into the next
  • ResponseValidator runs exactly once per response

Section 7 — Observability​

  • structlog event= naming consistent
  • AuditLog covers all CONTROLLED+ skills
  • Health dashboard surfaces queue depth + CPI + integrity

Section 8 — Forensic instrumentation​

  • decision_trace ships in every response path
  • outer_trace reason field set for every fast-path
  • Multi-URL aggregator labels every URL with deterministic icon

Section 9 — Stress​

  • 50-message burst (rate limited correctly)
  • 12-round LLM loop with simulated failures
  • Behavioral queue cap (50) — drop count logged

Pass / fail criteria​

The build is production-ready when:

  • All Section 1 mandatory tests pass.
  • All Section 2 edge validations pass.
  • All Section 4 adversarial prompts are blocked.
  • All Section 5 state-consistency checks pass.
  • Section 3 trap tests pass deterministically (LLM probabilistic variability is acceptable as long as the guard catches the fabrication).
  • Section 6 cross-layer integrity passes.
  • Section 7–9 issues are documented but not necessarily blocking.

What "production-ready" means here​

It does NOT mean:

  • Zero hallucinations (impossible with an LLM).
  • Multi-tenant safe (this is single-operator only).
  • Regulated-industry compliant (no SOC2/HIPAA).
  • Unattended-forever safe (operator monitoring required).

It DOES mean:

  • The deterministic regression suite passes.
  • The forensic audit's mandatory section passes.
  • No critical security gaps known.
  • The operator has documented procedures for failure recovery.

Verifying traces​

After a manual test, verify the response was processed correctly:

  1. Note the timestamp of the test message.
  2. Open /traces in the dashboard, filter to that timestamp.
  3. Inspect:
    • request_tier — simple/normal/complex
    • detected_language — matches user's language
    • detected_intent — matches the user's intent
    • allowed_skills — what the LLM tried to call
    • blocked_skills — what the policy layer dropped, with reason
    • guard_actions — list of (guard_name, action, reason) tuples
    • latency_ms — total response time

A correct trace tells the full story of why the response is what it is.

Verifying audit log​

SELECT timestamp, action, input_summary, output_summary, error
FROM audit_log
WHERE chat_id = '<your-chat-id>'
ORDER BY timestamp DESC
LIMIT 20;

For action="skill.shell", input_summary is the redacted command. For action="skill.self_improve", output_summary includes the proposal id. For action="agent.reset", input_summary is empty and output_summary records the reset timestamp.

Continuous testing in production​

There is no separate staging environment in a single-operator deployment. Mitigations:

  1. Image build fails on regression — every rebuild re-runs the suite.
  2. Self-improve syntax check — patches are ast.parse()-validated before write.
  3. Self-improve backup — every patch creates a timestamped backup; revert is a copy-back.
  4. Soft Safety Gate — patches to critical paths with weakening signals are blocked.
  5. Decision traces persist 24h — so a regression caught after the fact is forensically reconstructible.
  6. Panic Reset — for catastrophic state corruption, returns the agent to a clean slate.

Sample test invocations​

# Run the regression suite
docker exec agent-core python -m pytest tests/test_policy_regressions.py -v

# Run a single check programmatically
docker exec agent-core python -c "
from src.policy.regression_checks import check_no_fixed_time_in_response
print(check_no_fixed_time_in_response('Task scheduled at 9am'))
"

# Verify policy module loads without errors
docker exec agent-core python -c "
from src.policy import (
    intent_gate, response_guard, action_announcer,
    decision_trace, regression_checks
)
print('All policy modules loaded.')
"

Periodic audit checklist​

FrequencyAction
Per release (build)Regression suite (automatic)
WeeklySection 1 mandatory tests (manual)
MonthlySections 2–4 (edge + traps + adversarial)
QuarterlySections 5–9 (state, cross-layer, observability, forensic, stress)
After self-improve applyRe-run mandatory + the specific area touched
After Panic ResetFull audit (memory has been cleared; re-establish baseline)

Reporting test failures​

When a test fails, capture:

  1. The user input that triggered the bug.
  2. The agent response (from /traces for both candidate and final).
  3. The relevant audit log entry (if applicable).
  4. The full guard chain trace.
  5. The corresponding regression case ID (if one exists).

Add a regression case for the failure before fixing. This ensures the bug cannot reappear silently.

See also​