How to design a decision-grade pilot for agentic AI

Agentic AI pilots should be designed as authorization decisions rather than technology demonstrations, producing evidence that an agent can complete a specific workflow reliably, safely, economically, and with acceptable human effort. The framework recommends defining the intended workflow, users, autonomy level, acceptance criteria, expected business outcome, and rollout decision before testing, while treating autonomy as progressive from recommendations to bounded execution. Evaluation should measure end-to-end task completion across representative real-world scenarios, including ambiguous, adversarial, and out-of-scope cases, with repeated runs to reveal variability, segmented risk results, explicit uncertainty, and hard control gates for authorization, approvals, auditability, safety, and recovery. Teams should assess total human effort and cost per successful task, including review, rework, failures, infrastructure, and support, rather than relying on apparent time savings. A scorecard should link evidence to predefined thresholds, and a worked CI/CD failure example illustrates how an agent may be approved for lower-risk preparation tasks while remaining restricted for weaker-performing categories. GitLab Duo Agent Platform is presented as one environment for applying these principles across planning, coding, review, deployment, and security, using composite identities, tool governance, audit records, and configurable data boundaries. A pilot should ultimately produce a documented, limited authorization specifying the agent’s scope, permitted authority, controls, owners, monitored metrics, and conditions for suspension or reassessment.

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