Build useful AI agents without a full development stack — while still doing the hard systems work correctly.
A practical, platform-agnostic course for building useful bounded AI agents with visual and no-code tools while still performing the hard systems work correctly. Learners qualify opportunities before selecting tools; make architecture and data flow explicit; constrain tools, credentials, and side effects; engineer failure recovery; evaluate production behavior; and launch a maintainable agent with accountable human ownership. Vendor-specific capabilities are treated only as current, verified examples—not universal behavior or substitutes for API understanding, data modeling, security judgment, evaluation, accessibility, or maintenance. Each module ends with one portfolio-producing Decision Lab. Mastery requires at least 80% overall and satisfactory completion of the integrated capstone.
What you will be able to do
- Qualify and bound a realistic no-code AI-agent use case, including outcome, inputs, outputs, integrations, consequence level, success criteria, deterministic controls, and human responsibilities.
- Compare visual/no-code implementation approaches using evidence about capability, maintainability, permissions, data handling, cost, portability, accessibility, and operational fit.
- Produce an explicit visual architecture separating instructions, context, grounding, state, memory, deterministic application data, model reasoning, tools, and system boundaries.
- Configure tools, connectors, and API actions with defined contracts, least-privilege credentials, approval policies, and independent verification of consequential side effects.
- Implement resilient execution paths with validation, uncertainty branches, retry limits, duplicate-action protections, fallbacks, stop conditions, timeout handling, recovery, and human escalation.
- Evaluate a no-code agent with representative test sets, measurable rubrics, execution evidence, failure classification, security checks, and cost-latency-reliability analysis.
- Release, monitor, maintain, document, defend, and hand off a complete bounded no-code AI agent that meets an 80% mastery threshold.
The AI Agentic School Decision Lab
Don’t just connect blocks — design the system and defend the decisions. Each module ends with a realistic no-code AI-agent brief. You create a concrete visual architecture, integration plan, permission/control decision, evaluation artifact, or implementation recommendation, explain your rationale, receive rigorous rubric-based coaching and a simulated stakeholder challenge, then revise until you demonstrate 80% mastery. Your best module submissions become your personal No-Code AI Agent Build 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, security auditor, user-research participant, employer, or accreditation body.