Architecting & Operating Autonomous AI Agent Systems
Master autonomous agent system design: from ReAct decision loops and MCP tool dispatch, to long-term memory architectures, skill engineering, multi-layer safety guardrails, and evidence-based launch gates.
Autonomous Agent Runtime
ReAct Loop & MCP Protocol Sandbox
Ingests user intent and sets Triad of Control boundaries to prevent unauthorized actions.
5 Modules & 20 In-Depth Lessons
Each lesson is scoped to a core architectural decision, paired with Google AI paper diagrams and practical workshops.
The Intent Shift & The Autonomy Curve
Moving from syntax to intent, distinguishing traditional software from agents, decoding the 5 levels of autonomy, and 4 questions to validate agent opportunities.
The Agent Loop & Five Architectural Layers
The 5-step agent execution loop (Goal → Plan → Act → Observe → Iterate), deconstructing the 5 architectural layers, and incident triage workflows.
Engineering Discipline & Context as Infrastructure
The discipline spectrum from Vibe Coding to Agentic Engineering, static vs dynamic context architecture, and progressive disclosure mechanisms.
Reimagining SDLC & Agent Economics
Restructuring the 5 SDLC phases with AI assistance, product opportunity scoring rubric, Capex vs Opex economics, and model routing strategies.
Graduate with Concrete Evidence for Launch Decisions
The Capstone project integrates an ADK agent loop, MCP tool dispatch, packaged skills, a human approval gate, and a calibrated 12-scenario evaluation suite.
Everything You Need to Know
Who is the AI Agents for PM course designed for?
Designed for Product Managers, Tech Leads, and Solution Architects looking to design, build, and evaluate autonomous AI Agent systems - covering ReAct loops, Model Context Protocol (MCP), and multi-agent coordination.
Should I complete AI Literacy before starting this course?
We recommend completing AI Literacy or having a solid foundation in Tokens, Context Windows, and RAG to maximize your learning with advanced agentic architectures.
What is the Capstone Project requirements?
You will architect an end-to-end autonomous agent system featuring decision flow diagrams, MCP tool specifications, memory architectures, and rigorous Pass@k evaluation suites.
How is the curriculum structured and delivered?
5 in-depth modules containing 20 self-paced lessons, combining Google AI/Anthropic research diagrams with interactive decision workshops.
Ready to Architect Autonomous AI Agents?
Start immediately with Lesson 1: The Intent Shift & The Autonomy Curve in Agent Systems.