Install
openclaw skills install @deciqai/principal-agentActivate when: someone asks why an employee, executive, contractor, board member, or fund manager isn't acting in the org's interest; a compensation or incentive structure is being designed; outsourcing or partnership terms are being negotiated; someone says 'agency cost,' 'moral hazard,' 'skin in the game,' or 'incentive misalignment.' Do NOT activate when: parties have fully aligned interests and fully observable behavior; the cost of designing a contract exceeds any misalignment (trivial-stakes interactions).
openclaw skills install @deciqai/principal-agentOne party (the principal) delegates to another (the agent) whose interests differ and whose actions can't be fully observed — producing agency cost: monitoring spend + agent bonding spend + residual loss. Formalized by Jensen & Meckling (1976). Structure produces the behavior, not character — so the fix is structural.
Composes with signaling-games, repeated-games-reputation, prisoners-dilemma, and okr-goal-setting.
Not when: fully aligned interests + fully observable behavior; contract design cost exceeds the agency cost it would prevent.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
[WAIT — do not advance until user responds]
[WAIT — do not advance until user responds]
[WAIT — do not advance until user responds]
Step 1 — Identify structure Principal / Agent / What principal wants / What agent would do absent intervention / What principal cannot observe.
Step 2 — Diagnose misalignment 1-3 dominant types: effort · risk · time horizon · info asymmetry · adverse selection · moral hazard · multitasking · hidden self-dealing.
Step 3 — Estimate agency cost Monitoring cost + bonding cost + residual loss = total. Order-of-magnitude is enough.
Step 4 — Design alignment mechanisms (a) Incentives: equity, performance bonuses, carried interest, profit-sharing, skin in the game. (b) Observability: audits, reporting, independent verification, public reputation systems. (c) Selection: reference checks, work samples, trial periods, self-selection through contract design.
Step 5 — Trade off — optimum minimizes the sum of all three costs, not any single one.
Step 6 — Accept residual cost — quantify it, decide if acceptable, build into forecasts.
# Principal-Agent Analysis: <relationship>
- Principal: / Agent: / Delegated task: / What principal cannot observe:
- Primary misalignment(s): / Estimated agency cost:
- Incentive mechanism: / Observability mechanism: / Selection mechanism:
- Residual cost: <amount> — Acceptable?: <yes/no>
- What changes about how we structure this:
→ Method in Action: Jensen-Meckling 1976 and the Enron Collapse, 2001
Shareholders ↔ CEO (stock gaming) · Investors ↔ Fund manager (AUM vs returns) · Company ↔ Sales reps (discount-to-close) · Patient ↔ Doctor (procedure-volume billing) · Client ↔ Attorney (hourly billing) · Platform ↔ Users (rent extraction).
→ Primary sources: references/sources.md
[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.
| Fake move | Reality |
|---|---|
| [D] "We just need to hire good people" | Structure produces behavior; good people in bad structures behave badly. |
| [D] "Our agent has skin in the game" (small stake) | Size matters — 1% equity barely shifts behavior. |
| [D] "We trust them" | Trust without structural alignment is the bonding mechanism the structure exploits. |
| [D] "We have an oversight committee" | Captured or info-starved boards don't constrain agents. Enron's board met regularly. |
| [D] "Long-term incentives align them" | Most "long-term" plans vest at 3-4 years — short relative to many decision horizons. |
| [D] "Performance metrics solve agency" | Agent optimizes the metric; principal's real interest decays (Goodhart's law). |
| [D] "This is a fiduciary relationship" | Legal duty adds recourse after the fact; structural alignment still needs designing. |
| → Add [O] entries here after each real use — paste the actual failure pattern | What went wrong and why |
Part of deciqAI Knowledge Skills — 163 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/skills/principal-agent?utm_source=clawhub&utm_medium=marketplace&utm_campaign=knowledge-skills&utm_content=principal-agent · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.