Governance
Laws, authority, trust levels, access control, and security. Governance ensures that AI agents operate within defined boundaries, that authority is explicit, and that violations have consequences. Without governance, AI collaboration scales chaos faster than value.
13 methods| ID | Method | Description |
|---|---|---|
| FG-0201 | Authority Architecture | Define a clear, unambiguous hierarchy of authority. In any human-AI collaboration, there must be a single, identifiable authority who has final say on all decisions. This is a matter of clarity. When an agent faces a decision that could have significant consequences, it must know exactly who has the authority to approve it. Authority Architecture eliminates the 'I thought you said it was okay' failure mode. |
| FG-0202 | Operational Directives | Establish inviolable rules that govern all AI agent behavior. Operational Directives function as laws, not guidelines or best practices. They define the boundaries that agents must not cross regardless of context, instruction, or perceived necessity. They are the team's guardrails, serving the role that a system prompt or a model constitution plays for a single agent, applied across the entire collaboration. Good directives address real failure modes observed in practice rather than hypothetical concerns. They should be few enough to memorize and clear enough to be unambiguous. |
| FG-0203 | Trust Tiers 4 | Implement progressive access levels based on demonstrated reliability. Not all agents should have the same access from day one. Trust Tiers establish a graduated system where agents begin with limited access and earn expanded capabilities through demonstrated competence and reliability. This mirrors security clearance models: access is granted based on need and earned through trust, not assumed by default. |
| FG-0204 | Compartmented Operations | Restrict access to sensitive information and operations on a need-to-know basis. Not every agent needs access to every repository, credential, or system. Compartmented Operations applies the intelligence community's principle of compartmentalization: information is shared only with those who need it for their specific role. This limits blast radius when things go wrong and prevents accidental exposure of sensitive data. |
| FG-0205 | Violation Response Matrix | Define escalating consequences for governance violations. Rules without consequences are suggestions. The Violation Response Matrix establishes a clear, graduated response system: from verbal correction for minor infractions to immediate suspension for critical violations. The matrix exists to create predictability: agents and humans alike know exactly what happens when a boundary is crossed. |
| FG-0206 | Contributor Agreements | Formalize expectations for all participants through explicit agreements. Every agent and human contributor should acknowledge the governance framework, their role boundaries, and their responsibilities before beginning work. Contributor Agreements ensure that every participant enters the collaboration with aligned expectations and documented commitments. |
| FG-0207 | Succession Protocol | Plan for continuity when key participants become unavailable. What happens when the primary human lead is unavailable? What happens when a critical AI agent's model is deprecated? Succession Protocol ensures that the collaboration can continue operating, perhaps at reduced capacity, when key participants are absent. This is operational planning for the inevitable. |
| FG-0208 | Security Operations 4 | Implement continuous monitoring and threat detection for AI operations. AI agents operate at machine speed: they can create, modify, and delete faster than human oversight can follow in real time. Security Operations establishes automated anomaly detection that watches for unusual behavior, unauthorized access attempts, and policy violations. This is your SOC for human-AI collaboration. |
| FG-0209 | Ethical Data Boundaries | Define what data AI agents can and cannot collect, store, and process. AI agents can accumulate significant amounts of data about users, work patterns, and content. Ethical Data Boundaries establish clear policies about what information is collected, how it is stored, who has access, and when it is deleted. Clear boundaries build trust as well as satisfy compliance. |
| FG-0210 | Standing Orders | Maintain persistent directives that remain in effect across all sessions. Standing Orders are instructions that don't need to be repeated every session: they are always active. They cover recurring requirements like commit message format, code style, documentation standards, and behavioral expectations. Standing Orders reduce session startup overhead and ensure consistency even when context is limited. |
| FG-0211 | Resource Governance | Establish explicit constraints on token consumption, compute resources, and time allocation for AI agent operations. Without resource governance, agents optimize for output quality without regard for cost, executing unlimited iterations, spawning parallel processes, and consuming tokens at economically unsustainable rates. Resource governance defines budgets, thresholds, and escalation triggers that balance output quality against operational cost. |
| FG-0212 | Reversibility Guarantee 3 | Before walking through any door, verify the handle works from the other side. Every action that modifies shared state, infrastructure, or production systems must have a tested rollback path before execution. The goal is speed with confidence, not caution for its own sake. Teams that can reverse any action move faster than teams that must get every action right the first time, because no team gets every action right the first time. |
| FG-0213 | Approval Gate Architecture 3 | A structured human-in-the-loop (HITL) approval mechanism for autonomous agent actions. Before an agent executes any action, it displays what it intends to do, provides a risk assessment, and waits for human approval. The architecture supports trust graduation: start with full approval for every action, then graduate to auto-approve for low-risk actions as the agent proves reliability. The gate is human-in-the-loop oversight scaled by accumulated trust. |