Module 9
AI Agents & Autonomous Systems
Build AI that acts, decides, and executes on its own
Lessons
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9.1
Agent Architecture Fundamentals
Locked45 min
9.2
Building with LangGraph
Locked90 min
9.3
Tool Use & Function Calling
Locked75 min
9.4
Memory Systems for Agents
Locked60 min
9.5
Multi-Agent Orchestration
Locked90 min
9.6
Computer Use Agents
Locked75 min
9.7
Agent Reliability & Safety
Locked50 min
Expert Lens
Founder
Think like a founder: what user pain, distribution channel, pricing, and retention loop does this AI capability unlock?
Systems
Think like a first-principles systems builder: separate demos from durable products, and measure the bottleneck the model actually removes.
Infrastructure
Think like an AI infrastructure leader: account for data, latency, cost, reliability, evaluation, and deployment constraints before scaling.
Objectives
- Understand agent architecture and build production agents
- Create multi-agent collaborative systems
- Deploy autonomous business workflows
- Build agentic products users trust
Capstone
Autonomous AI Employee
Build and deploy a multi-tool agent that autonomously completes a complex recurring business task — research, execution, and reporting included.
A running agent completing a real workflow autonomously with full logging.
Real Examples
- Create a coding agent that can inspect a repo, propose a change, run tests, and produce a reviewable diff.
- Define tool permissions so the agent can help without silently taking destructive actions.
- Build a research agent with source collection, uncertainty labels, and human approval before publishing.
Mastery Checks
- Explain the core concept without buzzwords.
- Build a small working artifact, not just a note or prompt.
- Define an evaluation rubric and test against at least five edge cases.
- Estimate operational cost, failure modes, and human review requirements.