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Advanced Design Patterns in AI Agents

Stop reinventing the wheel. Get the definitive catalog of battle-tested agent architectures, from basic prompt chaining to metacognitive reasoning loops.

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Taught by an engineer who has shipped this

  • ISO 27001

    Led the engineering work behind the certification of a regulated EU platform.

  • Series A platform

    Architected the case-management product that became the business a €11.6M round was raised on.

  • 3x faster deploys

    Cut time-to-deploy by migrating to Kubernetes on GCP with deploy-on-merge.

The most comprehensive collection of AI agent design patterns available online. Prompt chaining, parallelization, multi-agent coordination, RAG pipelines, safety guardrails, metacognitive reasoning, each with production-ready Python code you can ship.

Battle-tested agent patterns. The reference guide that didn't exist until now.

What you'll ship

Real projects, not toy demos.

  • Pick the right agent architecture from 46 battle-tested patterns
  • Design multi-agent systems where specialized agents handle complex tasks
  • Build RAG pipelines that rewrite queries and filter irrelevant results
  • Add safety guardrails that catch failures before users see them
  • Apply reasoning patterns that make agents think before they act
  • Leave with production-ready code, not theoretical diagrams

What you'll learn

You finish able to:

  • Know which agent architecture to reach for (and why) for any problem
  • Design multi-agent systems that coordinate complex, real-world workflows
  • Add guardrails and evaluation that catch failures in production
  • Apply Tree of Thoughts, metacognition, and other advanced reasoning techniques
  • Walk away with pattern implementations you can adapt immediately

Curriculum

46 Battle-Tested Patterns. Production-Ready Code.

  1. 01

    Welcome & foundations

    Get started with AI agentic patterns, set up your environment, and learn the foundational patterns: prompt chaining and routing.

  2. 02

    Parallel processing & Self-improvement

    Run tasks simultaneously for speed, reflect on outputs, and integrate external tools into your agents.

  3. 03

    Planning & Multi-agent systems

    Decompose complex goals into plans, coordinate multiple specialized agents, and manage conversation memory.

  4. 04

    Integration & protocol patterns

    Connect to external services via MCP, set and monitor goals, handle exceptions gracefully, and incorporate human oversight.

  5. 05

    Knowledge & communication

    Retrieve external knowledge with RAG, enable agents to communicate, and optimize resource usage.

  6. 06

    Reasoning patterns

    Master chain-of-thought reasoning, self-correction, and structured problem decomposition.

  7. 07

    Safety, quality & management

    Implement guardrails, evaluate outputs with LLM-as-judge, monitor performance, prioritize tasks, and explore new topics.

  8. 08

    System architecture patterns

    Take a mid-course assessment, then build agentic RAG systems, orchestrate workflows, compose subgraphs, manage state machines, and create recursive agents.

  9. 09

    RAG pipeline patterns

    Execute code safely, rewrite queries for better retrieval, check relevancy, and process data for RAG pipelines.

  10. 10

    Advanced architectures

    Master Plan-Execute patterns, ReAct loops, blackboard systems, and dual memory architectures.

  11. 11

    Frontier reasoning patterns

    Explore advanced patterns: Tree of Thoughts branching, mental simulation, meta-controller orchestration, and graph-based knowledge memory.

  12. 12

    Self-improvement & capstone

    Build self-improving agents with RLHF and metacognition, then bring everything together in a capstone multi-pattern pipeline.

Who it's for

Is this for you?

AI engineers shipping agents

You build agents but pick architectures based on vibes, not knowledge.

Senior developers

You're tired of scattered blog posts. You want one definitive reference.

Tech leads & architects

You're designing AI systems and need patterns you can trust in production.

FAQ

Common questions.

  • Is this a beginner workshop?

    Not at all. It's intermediate. You should know basic LLM concepts and Python. Building AI Agents is a great warmup.

  • Are all the patterns covered in depth?

    Every single one. Code, explanation, and when to use it.

  • Can I use these patterns with any LLM?

    Yes. LiteLLM makes them provider-agnostic.

  • How is this different from Building AI Agents?

    Building AI Agents teaches fundamentals from zero. This one is a pattern catalog for when you already know the basics and need the playbook.