Learn how goal-driven AI systems plan, call tools, and verify results.
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Build a practical mental model for agent loops, tool boundaries, and reliable task completion. You will leave with small patterns for evaluating agent behavior without treating every prompt as magic.
2 Modules · 6 Lessons · 75 Minutes Total
Deconstruct agentic architecture, control loops, and state machine models.
Compare deterministic linear prompts against iterative ReAct agent cycles.
Model bounded state containers using Pydantic and explicit type annotations.
Implement a 3-step decision loop with exit criteria and iteration limits.
Connect external functions while enforcing parameter contracts and safety boundaries.
Register search and database retrieval functions with strict runtime typing.
Gracefully handle API timeouts, missing data, and invalid arguments without crashing the loop.
Audit decision traces to detect hallucinated parameters and ungrounded tool calls.
Build an agent that receives incoming customer tickets, queries a mock knowledge base using validated tools, categorizes issue severity, and produces a verified response draft.
Course Author & Industry Expert
Maya Iyer is a Principal AI Systems Architect with over a decade of experience building enterprise automation pipelines. She previously led agentic infrastructure teams at CloudScale AI.
No. The course includes mock client adapters compatible with local testing and standard Mesh endpoint configurations.
This course focuses on software engineering patterns—state management, tool schemas, and error boundaries—rather than prompt tuning tricks.
Yes! All architectural patterns taught are model-agnostic and rely on standard JSON schemas.