Prove that you can bound, evaluate, defend, and responsibly release an AI-agent system.
AI Agent Certification is a rigorous, performance-based professional certification from AI Agentic School. It verifies professional mastery of bounded AI-agent system design, evaluation, release judgment, and operational handoff rather than course attendance or framework memorization. Candidates complete exactly six modules, three topics per module, one Decision Lab after topic 3 in every module, six module assessments, and one comprehensive final assessment. Every Decision Lab produces a portfolio artifact requiring a concrete system decision, evidence-based rationale, and stakeholder defense. Candidates must earn at least 85% on each Decision Lab and assessment; work below mastery must be revised and resubmitted with a change record. The certification is vendor- and language-agnostic and does not imply university accreditation, government approval, vendor endorsement, or independent legal, safety, privacy, or security certification.
What you will be able to do
- Produce an AI Agent Certification Portfolio containing six Decision Lab artifacts, assessment evidence, revisions, and a complete bounded-agent capstone package.
- Differentiate prompts, assistants, deterministic workflows, automations, tool-using agents, and broader agentic systems, then justify the least-complex design that can reliably achieve the intended outcome.
- Define defensible goals, success measures, system boundaries, autonomy limits, prohibited actions, unacceptable failures, and conditions requiring redesign or rejection.
- Design an information architecture that separates instructions, task context, application state, conversation history, durable memory, retrieved knowledge, provenance, and external truth.
- Specify controlled tool execution using precise schemas, deterministic validation, least-privilege permissions, confirmation gates, credential protections, and independently verified evidence of side effects.
- Design bounded iteration, retries, fallbacks, stop conditions, human-control points, privacy protections, incident responses, escalation paths, and recovery behavior.
- Construct representative evaluations, inspect traces, classify failures, assess security and operational tradeoffs, and defend measurable release, monitoring, rollback, and ownership criteria.
- Integrate, evaluate, revise, and defend a platform-independent bounded-agent architecture or prototype specification, culminating in an evidence-based launch or no-launch recommendation and operational handoff.
The AI Agentic School Decision Lab
Don’t just understand AI agents—prove the system and defend the decisions. Each module ends with a realistic AI-agent certification brief. You create a concrete artifact or system decision, explain your rationale, receive rigorous rubric-based coaching and a simulated stakeholder challenge, then revise until you demonstrate 85% mastery. Your best module submissions become your personal AI Agent Certification Portfolio.
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, user-research participant, employer, auditor, or accreditation body.