Turn scattered AI-agent experiments into repeatable team practice with shared standards, controls, evaluation, ownership, and a reusable operating playbook.
A six-module professional training program that turns scattered AI-agent experiments into repeatable team practice. Learners establish shared standards for opportunity intake, architecture review, permissions and oversight, evaluation and release, lifecycle operations, governance, enablement, and rollout. The course is platform-agnostic and operational rather than builder-centered: learners repeatedly make review decisions, produce concrete team artifacts, and assemble an AI Agent Team Operating Playbook that can be adapted across multiple projects.
What you will be able to operationalize
- Apply a repeatable intake and triage standard that distinguishes prompts, assistants, deterministic workflows, conventional automation, tool-using agents, broader agentic systems, and cases where no AI solution should proceed.
- Produce and defend an agent opportunity brief containing the intended outcome, accountable owner, consequence level, autonomy boundary, dependencies, success criteria, and evidence required for progression.
- Use a shared architecture-review standard to document goals, instructions, context, state, memory, grounding, tools, structured interfaces, deterministic controls, verification, and component boundaries.
- Create risk-proportionate permission, data-handling, approval, human-oversight, escalation, and action-evidence standards without misrepresenting organizational policy choices as universal legal requirements.
- Build representative test sets, evaluation rubrics, failure taxonomies, acceptance thresholds, review gates, release criteria, rollback conditions, and change-control records for agent projects.
- Define lifecycle operations covering execution records, monitoring signals, incident response, regression testing, dependency changes, maintenance, handoff, ownership, rollback, and decommissioning.
- Integrate the course artifacts into an adaptable AI Agent Team Operating Playbook and defend its standards, decision rights, rollout sequence, and proportionality at a mastery threshold of 80%.
The AI Agentic School Decision Lab
Don’t just learn a standard—make the team decision and defend it. Each module ends with a realistic cross-functional AI-agent operating brief. You create a concrete standard, template, review artifact, control, or rollout decision, explain your rationale, receive rigorous rubric-based coaching and a simulated stakeholder challenge, then revise until you demonstrate 80% mastery. Your strongest module work accumulates into an AI Agent Team Operating Playbook.
The curriculum is source-traceable and human-reviewed before publication. AI is used where it adds learner value—as a research tool and interactive practice/evaluation partner—not as a claimed human author, auditor, regulator, employer, or accreditation body.