Lesson 1
Agentic Development: What It Is (and What It Isn’t)
Define agentic development, where it creates leverage, and when not to use it
Build production-ready agentic systems with contracts, tools, memory, evaluation, and guardrails.
Agentic development is the discipline of building software with AI agents as active collaborators — agents that can plan, use tools, maintain state, and iterate toward outcomes. This course is tool-agnostic and decision-first, focusing on the architecture patterns that make agentic systems reliable in the real world.
You will learn how to design agent runtimes, write behavior contracts, orchestrate multi-agent workflows, test and evaluate decisions, and ship agentic features that hold up under cost, latency, and safety constraints.
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Objectives
Prerequisites
Lessons
2 free preview lessons
Lesson 1
Define agentic development, where it creates leverage, and when not to use it
Lesson 2
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By the end of this course, you will have a production-ready agentic workflow — complete with tools, memory, evaluation harnesses, and governance checks. You’ll know how to decide when agents are appropriate, how to keep them safe, and how to ship them with confidence.
Lesson 1: Agentic Development: What It Is (and What It Isn’t)
Lesson 2: Agent Runtime Basics: Models, Loops, and State
Lesson 3: Agent Contracts: Roles, Prompts, and Guardrails
Lesson 4: Tooling & Action Interfaces
Lesson 5: Memory Systems: Short-Term, Long-Term, Retrieval
Lesson 6: Planning & Decomposition Patterns
Lesson 7: Multi-Agent Orchestration
Lesson 8: Human-in-the-Loop Controls
Lesson 9: Evaluation & Testing for Agents
Lesson 10: Reliability, Observability, and Safety
Lesson 11: Cost, Latency, and Scaling
Lesson 12: Shipping Agentic Products
Each lesson includes knowledge checks, recall cards, hands-on exercises, and real-world scenarios so you can apply the patterns immediately.
Agentic Product Sprint: Design and ship an end-to-end agentic workflow that drives a real business outcome. You will define roles, tool contracts, memory rules, evaluation criteria, and a production readiness checklist before launch.
Understand the building blocks of an agent runtime and the minimum viable loop
Lesson 3
Write clear role contracts, constraints, and output schemas to keep agents predictable
Lesson 4
Design tool APIs that agents can use safely with validation, retries, and fallbacks
Lesson 5
Choose memory tiers and retrieval patterns that keep agents grounded and useful
Lesson 6
Teach agents how to break work into safe, reversible steps with clear stop conditions
Lesson 7
Coordinate multiple agents with clear roles, handoffs, and shared state
Lesson 8
Design approval checkpoints, permission tiers, and audit trails for high‑risk actions
Lesson 9
Build regression harnesses and success metrics so agents improve over time
Lesson 10
Instrument agents with logs, retries, timeouts, and policy checks for production use
Lesson 11
Control token budgets, response time, and throughput as usage grows
Lesson 12
Package agent workflows into real product features with onboarding, trust, and governance