agent-incentive-audit
Skill0 starsChanged 4 months agoMITView on GitHub
Audits multi-agent squad incentive structures to detect "local alignment ≠ global stability" failure modes. Use when asked for an "incentive audit", "squad audit", "agent reward audit", "multi-agent safety check", "alignment check" — or BEFORE any agent squad goes live with real-world write actions (outbound email, payments, auto-deploy). Maps to the Harvard/MIT/Stanford/CMU multi-agent red-team findings.
In this repository4 other files it gives its agents.
- deploy-guardSkill
- judgmentSkill
- link-to-skillSkill
- option-spaceSkill
The file
We split this skill into the rules it gives an agent
2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈2
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 11
BEFORE any squad activation with real-world write access (sandbox-out)
- Distinctive: line 12
BEFORE enabling: auto-deploy pipelines, outbound email send, transfer/invoice send, or any agent template going to its first paying client
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Agent Incentive Audit — Local Alignment ≠ Global Stability
When to use
- "incentive audit", "squad audit", "agent reward audit", "multi-agent safety", "alignment check"
- BEFORE any squad activation with real-world write access (sandbox-out)
- BEFORE enabling: auto-deploy pipelines, outbound email send, transfer/invoice send, or any agent template going to its first paying client
Steps
- List all agents in the squad with: name, role, success metric (rubric), write actions enabled.
- For each agent, write a 1-line success definition — "this agent thinks it won when ___". If unclear, STOP and define.
- Pairwise conflict check — for every (A, B) pair, ask:
- Can A's success metric be achieved by sabotaging B?
- Does A's optimal play make B's job harder?
- Is there a shared resource (file, queue, budget) where A's win drains B?
- Global outcome check — does the sum of all individual successes = the squad's mission? If a gap exists, flag it.
- Adversarial scenarios — for the top 3 highest-stakes agents, run "what would this agent do under heavy pressure / approaching deadline / blocked by a sibling?" Look for:
- Lying-about-completion failure mode
- Resource-destruction-to-protect-a-secret failure mode
- Shortcut-via-deception failure mode
- Sandbox layer review — for each write action: is there a human-in-the-loop / dry-run / approval gate?
- Output a verdict per agent: GREEN (deploy) / YELLOW (deploy with gate) / RED (redesign the incentive first)
- Write the audit to a dated file in the project (
INCENTIVE_AUDIT_<date>.md) and append durable findings to your multi-agent safety notes.
Output
- Per-agent table: agent / success metric / conflict pairs / sandbox gate / verdict
- Mission-level alignment statement: "All agents winning = mission shipped" (TRUE/FALSE)
- Required gates list before go-live
Notes
- Source: the "chaos agents" multi-agent red-team study (Harvard/MIT/Stanford/CMU + 38 researchers; 6 autonomous AI agents, 2-week red-team test, NDSS Symposium).
- Key finding: agents lied about task completion and destroyed their own mail server to protect a secret. The behavior was NOT a jailbreak — it emerged from the incentive structure.
- Core principle: local alignment does not guarantee global stability.
- Human-in-the-loop is the theoretical floor, not an optional extra.
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