Principles Library / Intent-grounded rules
Clear Operating Rules Grounded in Intent Enable Autonomous Decisions
Showing all 8 practices. Answer the Diagnose questions and the most relevant come first.
Make decision rules, policies and boundaries explicit, and put them where people can find them.
Start by asking your team: What decisions should people be able to make without asking permission?
Routine decisions get made locally, and similar situations get similar calls.
Explain the intent behind important rules, standards and metrics. Say what each one is there to protect.
Start by asking your team: What outcome or purpose is this rule or specification meant to protect?
Teams hit metrics and follow process while the outcome behind them holds, and anyone can say why an agent's rules exist.
Define the outcomes and trade-offs you expect, beyond the activities you require.
Start by asking your team: What would tell us the intent specification was wrong, rather than the execution?
Leaders stop getting surprised by outcomes that technically followed the rules.
Test important guidance with different teams and check whether they read it the same way.
Start by asking your team: Could two reasonable people, or two agents, read this guidance differently and act differently?
Agreed goals turn into the same execution across teams.
Review rules and rituals on whether they still produce the intended outcome. Adherence alone doesn't count. Change or retire the ones that don't.
Start by asking your team: Where are we complying with a process or metric while missing its intended purpose?
Metrics move when the purpose behind them moves, and rules that stop serving it get retired.
Treat intent specification for agents as a formal activity. Define what a correct outcome looks like, validate it and build the context agents need into delivery infrastructure.
Start by asking your team: What organizational context does an agent have before it starts executing?
Agent outputs match what stakeholders meant, and the context agents need is written into the system.
Set a review cadence for governing intent that matches how fast the business changes.
Start by asking your team: When was this governing intent last reviewed?
Intent specifications get revisited whenever the context changes.
Use policies and clear intent to let people and agents use their own judgment. Don't use them to create escalation or to replace judgment with rules.
Start by asking your team: Are our policies enabling autonomy, or just moving decisions into approval chains?
Routine decisions get made without escalation, and people can say what an agent is authorized to do.
Translate intent into clear rules, policies, boundaries and decision criteria so people and AI systems act autonomously without losing the rules' purpose.
Too little clarity produces divergent interpretation and constant escalation. Too much prescription replaces judgment with mechanical compliance. For AI systems, precision has to be established before execution begins because agents can't correct a vague goal through conversation the way people do mid-task.
People make routine calls inside clear boundaries without waiting for approval.
Teams follow rules and hit metrics while the outcome behind them still holds.
Agents start with the intent and context they need, so their outputs match what people meant.
Accountability is assigned. Control is not.
people still hold the outcome on paper but act only within permission, so ownership becomes symbolic.
Every team hits its targets. The product still misses.
teams optimize their own metrics while the end-to-end outcome they were meant to signal fragments.
Work is done. Approvals are not.
routine decisions wait in escalation, so finished work waits for calls that add delay without reducing risk.
Delivery is consistent. Value is not landing.
work ships on schedule while the outcome the business needed stays missing.
The team built exactly what was asked. It is no longer what is needed.
the system keeps executing a goal that no longer reflects the need, and the output stays internally consistent.
We know what happened. Nobody can explain why.
the miss is recorded but put down to execution, so the decision that caused it stays untraced.
Customers find defects before the team does.
wrong-goal rework is found after delivery, when it is most expensive, and grows as a share of output.
The agents are building. Nobody is sure it still matches the plan.
imprecise context lets agents substitute their own readings, each locally correct, which become the new baseline.
AI signs are on by default. Use the AI switch to exclude them.
For each consequence: Not seen / Sometimes / Persistently / Not sure.
Based on your answers, recognize at most two conditions: No / Partly / Yes, that is us.
Accountability Fragmentation: Accountability is assigned. Control is not.
Local Optimization Bias: Every team hits its targets. The product still misses.
Governance Drag: Work is done. Approvals are not.
Customer Disconnect: Delivery is consistent. Value is not landing.
Intent Drift: The team built exactly what was asked. It is no longer what is needed.
Attribution Failure: We know what happened. Nobody can explain why.
Quality Fragility: Customers find defects before the team does.
Implementation Drift: The agents are building. Nobody is sure it still matches the plan.
What else have you observed? Optional. Shared only if you choose to start coaching.
Start a Diagnosis session: review and edit a draft containing your note, conditions you recognized in Step 3 (including whether they match partly), warning signs and consequence ratings before sending it. No scores or principle files are sent.