Why AI Projects Need Different Governance
Software project governance is built around a predictable model: requirements go in, code comes out, tests verify the code, you ship, you monitor. The management structures — status meetings, milestone tracking, change control — work because the relationship between input and output is deterministic and stable. AI agent projects violate this model in three important ways. First, outputs are probabilistic: the same request can produce different outputs, which means 'testing' is sampling, not verification, and quality is a distribution, not a pass/fail state. Second, the system can drift without any code change: if the underlying model changes, the data distribution shifts, or edge cases accumulate, behavior can degrade without anyone doing anything 'wrong.' Third, data sensitivity is embedded in the operating loop, not just in storage: every request processed by the agent involves real data flowing through potentially multiple third-party systems, which creates continuous compliance exposure rather than a point-in-time security check. Governance structures that treat an AI agent project like a website build will miss all of these risks until they materialize as production incidents. Use the Project Kickoff Guide to adapt these governance structures to your specific project context before your first meeting with the agency.
RACI for AI Agent Projects
A clear RACI prevents the two most common governance failures in AI projects: decisions being made by whoever happens to be in the room, and accountability gaps when things go wrong. For AI agent projects, the key decisions needing RACI coverage fall into five categories. Prompt and model changes: who must be Consulted before a prompt is changed in production? (Typically: technical lead, product owner, and compliance if in a regulated industry.) Who Approves? (Typically: product owner, with technical lead as Responsible.) Architecture decisions: who Approves a change in the LLM provider, orchestration framework, or vector database? (This should require Steering Committee approval — these are not routine engineering decisions.) Acceptance criteria: who is Accountable for defining and signing off on acceptance criteria before work begins? (Must be the business owner, not the agency.) Production incidents: who is Responsible for incident response, and who must be Informed within what timeframe? Data access decisions: who Approves adding a new data source to the agent's context? (This is a compliance and privacy decision, not just a technical one.) Document the RACI before kickoff and revisit it if the project scope changes materially.
Decision Rights for Model and Prompt Changes
In traditional software, code changes go through a change control process — PR review, testing, approval, deployment. In AI agent projects, prompt changes are functionally equivalent to code changes: they change system behavior, can introduce regressions, and should be version-controlled, reviewed, and tested before going to production. Establish a written policy for prompt change management before the project begins. At minimum, the policy should cover: (1) all prompt changes must be version-controlled in the same repository as the code, with a commit message describing the change rationale; (2) prompt changes to production agents require evaluation against the agreed acceptance criteria test set before deployment; (3) any prompt change that affects a decision the agent makes autonomously requires sign-off from the product owner, not just the technical lead; (4) prompt change history must be retained for a minimum of 90 days for audit purposes. Many agencies manage prompts in ad-hoc ways — changing them via API calls, storing them in internal config files not accessible to the client, or iterating without systematic evaluation. Your governance framework should require version control and evaluation for prompts as a contractual requirement, not an optional engineering practice.
Change Advisory Board and Monitoring Obligations
For production AI agents making consequential decisions, a lightweight Change Advisory Board (CAB) review is worth the overhead. The CAB doesn't need to be formal — it can be a 30-minute weekly or bi-weekly call between the product owner, technical lead, and agency project manager. Its purpose is to review changes proposed for production in the coming period, assess their risk, and approve, reject, or defer them. Changes that warrant CAB review: any model version upgrades, any prompt changes affecting decision logic, any changes to data sources the agent accesses, any changes to escalation thresholds, and any integration changes. Changes that don't need CAB review: bug fixes to non-decision-path code, infrastructure upgrades, monitoring improvements, and test additions. Separate from change governance, the agency must have monitoring obligations written into the contract: daily automated checks for accuracy regression against a sample of production requests, alerting when error rates exceed defined thresholds, and a weekly quality summary shared with the client. Agencies that don't offer proactive monitoring are implicitly asking you to detect your own problems — which is not a reasonable arrangement for a production system you're paying to have built and operated.
Steering Committee Cadence and Escalation Path
A steering committee for an AI agent project doesn't need to meet weekly — monthly is sufficient for most engagements. What it does need is a clear mandate, the right attendees, and a defined escalation path. Attendees: the business owner with budget authority, the internal technical champion, the agency account lead, and the agency technical lead. Agenda structure: project status against milestones (10 min), quality metrics review (10 min), risk register review (10 min), decisions required (15 min), open discussion (10 min). The risk register is the most important artifact the steering committee maintains. It should be a living document listing known risks, their likelihood, their impact, their current status, and the mitigation in place. AI-specific risks to always include: model vendor availability and pricing risk, data quality degradation risk, accuracy drift risk, key person risk on the agency side, and integration stability risk for each third-party system. The escalation path must be defined before it's needed: what constitutes an issue that bypasses the project manager and goes directly to the steering committee? The answer should include: production incidents causing user impact, budget overruns above a defined threshold (e.g., 15%), quality metrics falling below acceptance criteria in production, and key personnel changes at the agency.
Signs the Project Is Off-Track and How to Intervene
The earlier you intervene in a struggling AI agent project, the lower the cost of recovery. These are the early warning signs to watch for, in rough order of severity. Deliverable delays without written root cause analysis: one delay is manageable; a delay without a written explanation of what caused it and what's changing is a pattern indicator. Acceptance criteria negotiation at delivery time: if the agency is trying to redefine what 'done' means when you're reviewing deliverables, either the acceptance criteria weren't clear enough — your problem — or the agency knew they couldn't meet them — their problem. Either way, you need a structured conversation, not an informal concession. Evaluation results that aren't improving across iterations: accuracy metrics that plateau or regress indicate either the wrong approach, insufficient data, or an engineering problem. Don't accept 'we're still iterating' without a specific hypothesis about what will change. Communication cadence slipping: agencies under pressure tend to go quiet. If written weekly status updates stop or become vague, something is wrong. Request a specific status meeting with the technical lead — not the account manager. When you decide to intervene formally: document your concerns in writing, request a joint project retrospective within 5 business days, and bring the Performance Scorecard to the meeting.
Incident Response Planning for Production Agents
Every production AI agent will have incidents — wrong outputs, performance degradation, integration failures, model provider outages. The question is not whether incidents will happen but whether you have a plan before they do. Your incident response plan should cover five scenarios: (1) High error rate — agent producing clearly wrong outputs at scale: detection method, immediate response (disable autonomous actions, increase human review), investigation approach, rollback procedure. (2) Model provider outage: failover to backup provider or graceful degradation to manual process. (3) Data pipeline failure: what the agent does when its data sources are unavailable — fail closed, use cached data, or escalate to human. (4) Security incident — unauthorized data access, prompt injection attack, or data leakage: isolation procedure, notification requirements, forensic preservation steps. (5) Accuracy regression — gradual decline in output quality detected by monitoring before user complaints surface: evaluation protocol, root cause investigation, retraining or prompt revision process. The incident response plan should be a written document, agreed by both client and agency, before go-live. Specify exactly who gets notified for each severity level, within what timeframe, and via what channel. Ask the agency for their existing incident playbooks from prior production deployments — experienced agencies have them and should share sanitized versions as a starting point.
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