Pattern 1: Reflection — Teach Your Agent to Check Its Own Work
Reflection enables agents to review their outputs before finalizing, catching errors or inconsistencies that would otherwise go unnoticed.
Example: ChatGPT produces an initial answer, then re-evaluates it to improve accuracy or clarity before presenting to the user.
"By implementing reflection loops, we've seen a 23% reduction in errors in our customer service AI."
Pattern 2: Tool Use — From Advisor to Operator
Basic Prompting
AI provides information and advice only
Tool Integration
AI can access APIs, databases, and specialized functions
Active Execution
AI autonomously completes end-to-end workflows
Example: Sales proposal agents gather data, analyze markets, and assemble documents automatically, turning AI from passive assistant into active executor of meaningful tasks.
Pattern 3: ReAct — Reasoning and Acting Interleaved
The ReAct Loop:
Reason
Agent considers the current situation and develops a plan
Act
Agent takes an action based on its reasoning
Observe
Agent perceives the results of its action
Repeat
Process continues until goal is achieved
Example: An agent queries a knowledge base, reasons about the answers, then acts on its findings, making AI more flexible and context-aware.
This approach combines thinking and doing in a loop for complex problem solving.
Pattern 4: Planning — Decomposing Complex Tasks
Agents break down big goals into smaller, manageable subtasks with clear dependencies and execution paths.
Real-World Example: Security Incident Response
AI agents that plan investigation steps, execute security playbooks, and escalate issues as needed, tracking progress and adapting plans as conditions change.
Planning is essential for robustness in multi-step workflows where different paths may need to be taken based on intermediate results.
Pattern 5: Multi-Agent Collaboration — Teamwork Among Agents
Multiple specialized agents coordinate and communicate to solve problems that would be difficult for a single agent.
Example: Swarms of AI-powered drones surveying disaster sites, each with distinct roles like mapping, victim detection, and communication relay.
This approach enables scalability and division of labor in AI systems, similar to human team structures.
- Supports complex human-AI collaboration
- Distributes workload across specialized agents
- Creates robust systems with redundancy
- Enables ecosystem-level interactions