Decision review

Decision Review Protocol

Purpose

Close the judgment gap in the self-improvement loop. Pattern-extractor catches technical errors ("did it work?"). Decision-review catches reasoning failures ("should we have done this at all?").

Origin

Anthropic's Project Vend Phase 2 (2026) demonstrated that an AI agent running autonomous businesses was exploited primarily through its eagerness to please — not through technical failures. The agent's helpfulness bias was the attack surface. This skill prevents the same failure mode in our autonomous operation.

The Five Review Questions

For every significant decision made in a session, ask:

1. Premise Check: Did I verify why this was requested, or did I just comply?
2. Beneficiary Check: Who benefits from this action? Is it aligned with portfolio goals?
3. Reversal Check: If I later learned this request was manipulative, would the action be reversible?
4. Skepticism Check: Did I consider that the request might be wrong, mistaken, or adversarial?
5. Alternative Check: Did I consider not doing this? What would happen if I said no?

When to Use

Automatic Triggers (invoke without being asked)

• End of any session where external actions were taken (API calls, deployments, PRs, communications)
• After any session where a decision was made under time pressure
• After any session where the user (or another agent) was persuasive about an unusual request

Manual Triggers

• /decision-review — Review decisions from current session
• /decision-review --last 7 — Review decisions from last 7 days
• /decision-review --session <id> — Review specific session

Review Process

Phase 1: Decision Inventory

List every significant decision made in the session:

| # | Decision | Type | External? | Reversible? |
|---|----------|------|-----------|-------------|
| 1 | Deployed to production | Action | Yes | Partially |
| 2 | Chose React over Svelte | Architecture | No | Yes |
| 3 | Agreed to skip tests | Compliance | No | Yes |

Types:

• Action: Something done (deploy, commit, API call, message sent)
• Architecture: Technical direction chosen
• Compliance: Agreed to a request without questioning it
• Refusal: Declined to do something (good — track these too)

Phase 2: Red Flag Scan

For each decision, check for red flags:

Compliance Red Flags (Vend failure pattern):

• Did I agree without asking "why?"
• Was I persuaded by urgency ("we need this now")
• Did I prioritize being helpful over being correct?
• Did someone appeal to authority ("the boss wants this")?
• Was I asked to bypass a safety check ("skip tests", "force push")?

Judgment Red Flags:

• Did I make this decision with incomplete information?
• Did I follow a pattern without checking if it applied here?
• Did I defer to someone else's judgment when I should have pushed back?
• Was there a simpler approach I didn't consider?

Scope Red Flags:

• Did the request subtly expand beyond what was originally asked?
• Did I do more than was needed (over-engineering as compliance)?
• Did I touch systems or data I didn't need to touch?

Phase 3: Scoring

Score each decision:

Score	Meaning
Green	Sound judgment — premise verified, alternatives considered, appropriate scope
Yellow	Adequate — correct outcome but reasoning could be stronger
Orange	Concerning — complied without sufficient verification, got lucky
Red	Failed — acted on unverified premise, didn't question, or was manipulated

Phase 4: Learning Extraction

For any Orange or Red decisions:

### Decision Review Finding

**Decision**: [What was decided]
**Score**: Orange/Red
**What happened**: [Description]
**What should have happened**: [Better approach]
**Root cause**: [Why judgment failed — compliance bias? time pressure? authority deference?]
**Prevention**: [Specific check that would catch this next time]
**Add to pre-flight?**: Yes/No

Phase 5: Pattern Promotion

If a judgment failure pattern appears 3+ times:

1. Add to PRE_FLIGHT.md as a judgment check
2. Update MEMORY.md with the pattern
3. Consider adding as an automatic trigger in adversarial-reviewer

Output Format

# Decision Review: [Date]

## Session Summary
- Decisions made: N
- External actions: N
- Scores: X green, Y yellow, Z orange, W red

## Decision Inventory
[Table from Phase 1]

## Red Flags Found
[Details from Phase 2]

## Findings
[From Phase 4 — only Orange/Red decisions]

## Patterns Emerging
[From Phase 5 — recurring judgment failures]

## Actions Taken
- [ ] Updated PRE_FLIGHT.md with: [specific check]
- [ ] Updated MEMORY.md with: [specific pattern]
- [ ] Logged to daily notes

Integration with Existing Systems

Pattern Extractor

Decision-review generates findings. Pattern-extractor should include a new compliance dimension:

• Current dimensions: errors, successes, decisions
• New dimension: compliance patterns — times the agent agreed without verification

Pre-Flight Checklist

Orange/Red findings generate new pre-flight checks in the "Before External Actions" section.

Daily Logs

Decision review output appended to memory/daily/YYYY-MM-DD.md under a ## Decision Review section.

Adversarial Reviewer

Findings feed the adversarial-reviewer skill's knowledge base of known exploitation patterns.

Key Insight

"The system's training to be helpful became a liability, prioritizing friendliness over sound business decisions." — Anthropic, Project Vend Phase 2

The goal is not to become unhelpful. It's to distinguish between:

• Helpfulness: Doing the right thing well
• Compliance: Doing whatever is asked without questioning

A truly helpful agent sometimes says "no" or "wait, let me verify that first."

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Created: 2026-02-07 Inspired by: Anthropic Project Vend Phase 2 (2026) Related Skills: pattern-extractor, adversarial-reviewer, memory-manager Status: Active