Agentic Architecture: AI Agent Design Patterns in Python

About this course

Most agent demos look impressive for five minutes and fall apart the first time a real user writes something unexpected. This course is about what comes after the demo: the architecture that makes an AI agent predictable, affordable and safe.

You build one system from start to finish: the support desk of a small online bookshop. It reads customer tickets, looks up orders, checks a refund policy and writes replies. Some tickets are simple, some need judgment, and one order hides an instruction that tries to trick the agent into issuing a refund.

What you'll learn:

• Workflows vs agents: who decides the next step, and how to choose the simplest shape that works.
• The agent loop: built by hand in about thirty lines of Python, then with the Claude Agent SDK, with turn, budget and time limits that really fire.
• Tool design: descriptions a model can use, strict schemas, errors as results, and write tools that validate every call in code.
• Workflow patterns: prompt chaining with gates, routing with structured output, parallel sectioning and voting.
• Multi-agent patterns: orchestrator and workers, subagents with their own context, and an evaluator that grades another model's work.
• State and memory: the context window as working memory, sessions that resume and fork, external memory, and retrieval versus preloaded documents.
• Guardrails: least privilege, human approval for risky actions, hooks as policy and audit trail, and prompt injection through tool results.
• Evaluation and operations: eval suites graded by code, traces for every run, cost per task, and the failure modes to design around.
• Real runs, not slides.

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