46 Production-Ready Agent Patterns
Stop reinventing the wheel. Get the definitive catalog of battle-tested agent architectures — from basic prompt chaining to metacognitive reasoning loops.
What You Will Learn
- ✓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 46 pattern implementations you can adapt immediately
Who Is This For
- 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.
Curriculum
Welcome & Foundations
3 lessons
Parallel Processing & Self-Improvement
3 lessons
Planning & Multi-Agent Systems
4 lessons
Integration & Protocol Patterns
4 lessons
Knowledge & Communication
3 lessons
Reasoning Patterns
3 lessons
Safety, Quality & Management
5 lessons
Data Validation & Structured Output
2 lessons
System Architecture Patterns
5 lessons
RAG Pipeline Patterns
4 lessons
Advanced Architectures
5 lessons
Frontier Patterns & Capstone
7 lessons
Frequently Asked Questions
- Is this a beginner workshop?
- No — it's intermediate. You should know basic LLM concepts and Python. Building AI Agents is a great warmup.
- Are all 46 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 — you already know the basics, now you need the playbook.
46 Production-Ready Agent Patterns
46 battle-tested agent patterns. The reference guide that didn't exist — until now.
Chosen by engineers designing production AI systems
The Reinvention Trap. Sound familiar?
Vibe-Based Architecture
You're building an agent system, but you chose the architecture based on a blog post you read rather than a rigorous evaluation of trade-offs.
The LangChain Maze
You're trying to implement a specific pattern, but you're fighting the framework's abstractions instead of just writing the logic.
Brittle Abstractions
Your custom agent loop works for 80% of cases, but spectacularly fails on edge cases because you didn't implement proper state recovery.
Theory, No Practice
You read the Tree of Thoughts paper, but when it came time to implement it, you realized you had no idea how to structure the code.
The fix isn't another framework.
It's a pattern library.
Senior engineers aren't better because they know more syntax. They're better because they've:
Just like the Gang of Four defined OOP, these 46 patterns define AI Engineering.
Design Patterns for AI.
The reference guide that didn't exist.
We've cataloged 46 distinct agent architectures. For every pattern, we provide the theory, the use cases, and the pure-Python implementation using LiteLLM.
The Pattern Mastery Path
From basic chains to frontier cognition.
Foundations
Master prompt chaining, routing, and parallel execution for deterministic workflows.
RAG & Knowledge
Implement query rewriting, semantic routing, and self-correcting RAG pipelines.
Multi-Agent
Design systems with supervisors, sequential processes, and dynamic agent generation.
Frontier Reasoning
Build Tree of Thoughts, Reflexion, and Metacognitive controllers for the hardest tasks.
46 Battle-Tested Patterns. Production-Ready Code.
Welcome & Foundations
Get started with AI agentic patterns, set up your environment, and learn the foundational patterns: prompt chaining and routing.
Welcome to AI Agentic Patterns
Overview of the course, what you will learn, and environment setup
Prompt Chaining
Chain multiple LLM calls sequentially, passing output from one step as input to the next
Routing
Classify inputs and route them to specialized handlers based on content or intent
Parallel Processing & Self-Improvement
Run tasks simultaneously for speed, reflect on outputs, and integrate external tools into your agents.
Parallelization
Run multiple LLM calls simultaneously and aggregate results for faster processing
Reflection
Have an LLM review and improve its own output through iterative self-critique
Tool Calling
Give your agent access to external tools and APIs through function calling
Planning & Multi-Agent Systems
Decompose complex goals into plans, coordinate multiple specialized agents, and manage conversation memory.
Planning
Break complex goals into structured, executable plans with steps and dependencies
Multi-Agent Coordination
Design specialized agents with distinct roles that collaborate on complex tasks
Memory Management
Store and retrieve conversation history, user preferences, and context across sessions
Foundation Assessment
Test your knowledge of the first 8 patterns before moving to more advanced topics
Integration & Protocol Patterns
Connect to external services via MCP, set and monitor goals, handle exceptions gracefully, and incorporate human oversight.
Model Context Protocol (MCP)
Standardized protocol for connecting AI systems to external tools and data sources
Goal Setting & Monitoring
Set measurable goals, track progress, and adapt strategy based on metrics
Exception Handling & Recovery
Build resilient agents with fallback strategies, retry logic, and graceful error recovery
Human-in-the-Loop
Escalate low-confidence or sensitive decisions to human reviewers
Knowledge & Communication
Retrieve external knowledge with RAG, enable agents to communicate, and optimize resource usage.
Knowledge Retrieval (RAG)
Enhance LLM responses by retrieving relevant external knowledge before generation
Inter-Agent Communication
Enable structured message passing between agents through a communication hub
Resource Optimization
Dynamically select models and strategies based on task complexity to reduce cost
Reasoning Patterns
Master chain-of-thought reasoning, self-correction, and structured problem decomposition.
Chain of Thought
Enable step-by-step reasoning to improve problem-solving accuracy
Self-Correction
Have the LLM review its output, identify errors, and generate corrected versions
Problem Decomposition
Break complex problems into manageable sub-problems with identified dependencies
Safety, Quality & Management
Implement guardrails, evaluate outputs with LLM-as-judge, monitor performance, prioritize tasks, and explore new topics.
Guardrails & Safety
Implement input validation, content filtering, and output safety checks
Evaluation (LLM-as-Judge)
Use an LLM to systematically evaluate response quality, safety, and accuracy
Monitoring
Track performance metrics, detect anomalies, and maintain system health
Prioritization
Intelligently prioritize tasks based on urgency, importance, and deadlines
Exploration & Discovery
Autonomously explore topics, generate hypotheses, and discover new knowledge areas
Data Validation & Structured Output
Use Pydantic for type-safe LLM outputs and take a mid-course assessment.
Pydantic Validation
Enforce type safety and validate structured LLM outputs with Pydantic schemas
Mid-Course Assessment
Comprehensive checkpoint covering patterns 1-22
System Architecture Patterns
Build agentic RAG systems, orchestrate workflows, compose subgraphs, manage state machines, and create recursive agents.
Agentic RAG
Build intelligent RAG that decides when to retrieve, what to retrieve, and assesses response quality
Workflow Orchestration
Orchestrate complex multi-step workflows with dependencies and error handling
Subgraphs
Build modular, reusable workflow components that compose into larger systems
State Machines
Manage agent behavior with clear states, event-driven transitions, and defined rules
Recursive Agents
Build agents that recursively decompose and solve problems with depth control
RAG Pipeline Patterns
Execute code safely, rewrite queries for better retrieval, check relevancy, and process data for RAG pipelines.
Code Execution
Safely execute dynamically generated code with validation and sandboxing
Query Rewriter
Optimize and expand queries for better document retrieval
Relevancy Check
Filter and score retrieved content for relevance and quality
Data Processing & Anonymization
Clean, validate, and chunk data for RAG, plus anonymize PII for privacy
Advanced Architectures
Master Plan-Execute patterns, ReAct loops, blackboard systems, and dual memory architectures.
Plan Executor
Parse, validate, and execute structured plans with dependency tracking and adaptation
ReAct (Reason + Act)
Interleave reasoning and action in a Think-Act-Observe loop for dynamic problem solving
Plan-Execute-Verify
Plan steps, execute them, and verify each result before proceeding
Blackboard Systems
Collaborate through a shared memory repository with dynamic agent activation
Episodic & Semantic Memory
Dual memory combining conversation history (episodic) and structured knowledge (semantic)
Frontier Patterns & Capstone
Explore cutting-edge patterns — Tree of Thoughts, meta-controllers, graph memory, RLHF, metacognition — and build a capstone multi-pattern pipeline.
Tree of Thoughts
Explore multiple reasoning paths systematically and find the optimal solution
Mental Loop & Dry Run
Simulate actions mentally before executing and validate with safety checks
Meta-Controller & Ensemble
Route tasks to specialists and aggregate diverse perspectives
Graph Memory
Store knowledge as a graph of entities and relationships for multi-hop reasoning
RLHF & Self-Improvement
Build a self-improving loop where outputs are critiqued and high-quality examples are stored
Cellular Automata & Metacognition
Emergent behavior from simple local rules, and agents that reason about their own capabilities
Capstone: Multi-Pattern Pipeline
Build a complete pipeline combining guardrails, routing, planning, RAG, ReAct, reflection, and evaluation
Detailed Curriculum
See every system, every week, in detail before you decide.
Amateurs guess. Engineers apply patterns.
APIs are easy.
Anyone can call OpenAI and parse a JSON response.
Architecture is hard:
- Know when to use a Router vs a Semantic Router
- Design a robust Prompt Evaluation Validator (PEV)
- Implement a Blackboard architecture for multi-agent state
- Build a self-improving Prompt Optimizer loop
Stop writing bespoke agent loops. Start applying battle-tested patterns.
Learn From Someone Who Actually Does This
I am the Head of Engineering at Jobbatical (EU Tech), with 8+ years of leadership and 15+ years of total experience in the software industry.
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Frequently Asked Questions
Is this a beginner workshop?
Are all 46 patterns covered in depth?
Can I use these patterns with any LLM?
How is this different from Building AI Agents?
8 hours from now:
Stop guessing. Start architecting.