Design agentic workflows that combine AI judgment with deterministic control, state, routing, tools, approvals, recovery, observability, and real-world reliability.
Agentic Workflows Course is a six-module professional course about the orchestration layer that coordinates AI judgment with deterministic application logic, durable state, routing, bounded agents, tools, approvals, recovery, observability, and human control. It assumes learners already understand basic workflow concepts and does not reteach generic inputs-rules-actions-outputs foundations or focus on building one standalone agent. Learners progressively assemble an Agentic Workflow Systems Portfolio and complete an integrated capstone. Every objective produces an observable decision, specification, test, artifact, or defended tradeoff. Fictional scenarios and synthetic data are explicitly labeled. Mastery requires a score of 80% or higher.
What you will be able to do
- Classify a proposed system as deterministic automation, AI-assisted workflow, bounded AI agent, agentic workflow, or multi-agent system and defend the classification with an explicit control-flow analysis.
- Produce an agentic-workflow opportunity brief that defines the end-to-end outcome, system boundary, agency placement, success criteria, constraints, and reasons not to use agentic orchestration.
- Translate a complex process into an operable orchestration model with stages, durable state, legal transitions, routing rules, queues, handoffs, checkpoints, and explicit stop conditions.
- Specify bounded agents, direct model calls, deterministic functions, retrieval components, external tools, and human reviews through structured contracts, permissions, data boundaries, and execution-verification rules.
- Design bounded approval, retry, timeout, idempotency, fallback, compensation, escalation, and human-takeover policies for partial, ambiguous, and terminal failures.
- Build representative end-to-end test sets, evaluation rubrics, failure taxonomies, trace plans, operational metrics, and release gates that assess the workflow as a production system.
- Defend cost, latency, throughput, reliability, security, autonomy, and maintainability tradeoffs without assuming that more agents or greater autonomy produce a better system.
- Deliver an implementation-ready capstone architecture or working-prototype specification with governance, launch, rollback, maintenance, ownership, and stakeholder-handoff plans, earning at least 80% on the integrated mastery rubric.
The AI Agentic School Decision Lab
Don’t just diagram agentic workflows—make the orchestration decisions and defend the system. Each module ends with a realistic workflow architecture brief. You create a concrete orchestration artifact or system decision, explain your rationale, receive automated rubric-based coaching and a simulated stakeholder challenge, then revise until you demonstrate 80% mastery. Your best module submissions become your personal Agentic Workflow Systems Portfolio.
The curriculum is source-traceable and human-reviewed before publication. AI is used where it adds learner value—as a research tool and automated interactive practice/evaluation partner—not as a claimed human author, user-research participant, employer, auditor, or accreditation body.