🎯 By the end of this module, you will:
Directing a Hollywood Film vs. A Lone Solo Creator
In production, you cannot deploy a single unconstrained loop and expect it to magically handle everything. Big systems require proven design patterns to ensure predictability, auditability, and speed.
Just as software engineering evolved MVC and microservices, agentic AI has developed canonical patterns: ReAct, Reflection, Orchestrator-Workers, and Checkpoint Time-Travel.
One person attempts to write the script, operate the camera, perform the stunts, record audio, and color grade simultaneously. They get overwhelmed, miss crucial flaws, and produce a shaky final cut.
A specialized director coordinates a cinematographer, a stunt coordinator, and a dedicated film editor. Independent review passes ensure cinema-quality perfection before the film hits theaters.
"Beginners always rush to build complex 8-agent swarm systems. In production, 80% of problems are solved with a simple 2-step ReAct pattern or an Actor-Critic reflection pass. Master the foundational patterns before adding multi-agent complexity!"
The 4 Foundational Design Patterns
Select a design pattern below to inspect its topology and code implementation:
Actor drafts an initial output; an independent Critic scores it against strict rubrics for self-healing.
# Reflection Loop (Self-Correction)
draft = actor.generate_code(spec)
critique = critic.evaluate(draft, rubric=["security", "performance"])
while critique.has_vulnerabilities and turns < max_retries:
draft = actor.refine(draft, feedback=critique.issues)
critique = critic.evaluate(draft)"The Reflection pattern is simply the relationship between a novelist and their copy editor. The author writes freely with creative flow; the editor reads with critical eyes for plot holes and typos. Together, they produce masterworks!"
When to Use Which Pattern
Selecting the right pattern for your problem balances accuracy against latency and token cost:
Best for: Information retrieval, customer support lookups, and tasks requiring 1 to 5 sequential tool queries.
Best for: Complex code generation, translation, legal contract drafting, and high-consequence calculations.
"Exhaust single-agent patterns (ReAct with structured outputs and Reflection) before reaching for multi-agent swarms. Single agents are 10x easier to debug, have predictable latency, and don't suffer from inter-agent communication drift!"
Proven Agentic Design Patterns
Select a design pattern to inspect its architectural topology, real-world applications, and engineering trade-offs
Planning & Replanning
Deconstruct, Order, and Dynamically Adapt
The agent explicitly breaks an ambiguous objective into an ordered sequence of discrete subtasks before executing, and dynamically refines the plan as new observations arrive.
Enterprise Tech Migration: (1) Audit dependencies ➔ (2) Check version compatibility ➔ (3) Rewrite deprecated APIs ➔ (4) Run test suite ➔ If tests fail, inject new bugfix subtask dynamically.
Higher initial latency before first action; potential over-planning on trivial requests.
Interactive Design Patterns & Time-Travel Studio
Test the original ReAct reasoning trajectory, the Actor-Critic reflection loop, and human state rewinding
"I need to find out which book inspired 'Blade Runner' and who authored it."
"Observation confirms the book is 'Do Androids Dream of Electric Sheep?' and the author is Philip K. Dick. Ready to output final answer."
"Philip K. Dick wrote 'Do Androids Dream of Electric Sheep?', which inspired 'Blade Runner'."
Hallucinates ungrounded facts when missing external real-world knowledge.
Grounds reasoning in verified tool observations, cutting hallucinations by more than half!
1. Loop Exit Challenges: Can get stuck cycling the same thoughts without termination.
2. Single-Agent Bottleneck: Performance degrades as tool counts exceed 15-20 tools.
"When using Reflection, consider having a different, more powerful model act as the Critic (e.g., fast model drafts initial code; reasoning model critiques security). Models struggle to spot their own subtle blindspots when self-evaluating in the same prompt!"
Splitting a simple workflow into 5 autonomous agents talking to each other. Inter-agent chatter explodes token costs and leads to conversational deadlocks.
Fix: Start with a single ReAct loop or deterministic DAG. Only branch to multi-agent when distinct tools or security scopes demand it.
Allowing an Actor and Critic to endlessly nitpick stylistic trivialities without a stopping threshold.
Fix: Cap reflection passes at `max_reflections = 3`. If quality thresholds aren't met in 3 rounds, return the best candidate.
Summary Checklist
Module 1.8: Short-Term and Long-Term Agent Memory
Explore in-context short term scratchpads vs persistent vector & relational long-term memory.