TL;DR
Most AI agents fail because they try to do too much in one shot. The best AI systems break work into Planning and Execution stages. This guide shows you the 5 proven patterns used by Anthropic, OpenAI, and GPT-4 — and how to build them yourself
Why Most AI Agents Fail
You've probably experienced this:
Ask an AI to "setup automations for you" → it hallucinates steps
Ask it to "process this data" → it misses context
The problem isn’t the AI model, its the architecture
Standalone LLMs have no memory, no tools, and no coordination. They work in isolation, making decisions without full context
“Agents begin their work with interactive discussion with the human user. Once the task is clear, agents plan and operate independently.”
The 5 AI Agent Patterns You Need to Know
Based on research from Anthropic, OpenAI, LangChain, and production AI systems, here are the patterns that work:
1. Prompt Chaining
What it is: Break complex tasks into sequential steps. Each step's output becomes the next step's input
When to use it:
Data Processing pipelines
Multi-Step Transformations
Document Analysis Workflows
Example:
A user uploads raw data → AI extracts key fields → cleans the data → analyzes trends and generates a report. Each step builds on the last
Prompt Chaining in Lamatic: API Request → Extract Data → Clean Data → Generate Report → Response
2. Routing
What it is: Use an AI to classify intent and route to specialized handlers
When to use it:
Customer support (route by intent)
Content processing (route by type)
Multi-purpose assistants
Example:
Customer Sends “I want to upgrade my plan“ → Classifier detects “sales” intent → Routes to Sales agent (not Support or Technical)
Routing in Lamatic: Chat Trigger → Intent Classifier → Condition branches to Sales Agent, Support Agent, or Technical Support
3. Orchestrator - Workers
What it is: A central AI breaks down tasks and delegates to specialized worker agents
When to use it:
Complex projects with multiple components
Research and synthesis tasks
Code generation with multiple files
“The orchestrator dynamically breaks down tasks, delegates them to worker LLMs, and synthesizes their results.”
Example:
User asks for a blog post → Orchestrator assigns: Research Agent (gather facts) → Writer Agent (draft content) → Editor Agent (polish) → Synthesize Results
Orchestrator-Workers in Lamatic: API Request → Orchestrator → Branching to Writer Agent, Editor Agent, Research Agent → Synthesize Results → Response
4. Plan and Execute
What it is: First create a complete plan, then execute step by step.
Why it is powerful:
Users can approve before Execution
Each step of Execution has the “Plan”
Results are predictable
This is the pattern used by:
Cursor AI
Devin(Cognition)
GPT Pilot (33k GitHub Stars)
Example:
User says "Book my tickets to Paris" → Planner creates plan → Condition checks if clarification needed → If clear, shows plan for approval → User confirms → Executes step by step.
Plan-and-Execute in Lamatic: Chat Trigger → Planner → Condition → Ask Questions OR Show Plan → Execute Plan → Response
5. Evaluator - Optimizer
What it is: Generate output, evaluate it, refine until it meets criteria
When to use it:
Content that needs quality checks
Code that needs testing
Outputs that need validation
Example:
AI generates content → Evaluator scores it (1-10) → If score < 8, Refiner improves it with feedback → If score ≥ 8, output passes quality check.
Evaluator-Optimizer in Lamatic: API Request → Generator → Evaluator → Condition → Quality Passed OR Refiner → Refined Output → Response
The Secret: Combine Patterns for Maximum Power
The best AI systems don't use just one pattern — they combine them:
Stage | Pattern | Purpose |
|---|---|---|
Intent | Routing | Understand user wants |
Planning | Evaluator-Optimizer | Clarify and confirm plan |
Execution | Orchestrator-Workers | Generate each component |
Validation | Prompt Chaining | Check and refine output |
“Start with the simplest solution and only increase complexity when necessary“
Key Takeaways
Don't use bare LLMs — they lack memory, tools, and coordination
Break complex tasks into stages — Planning → Execution
Validate before output — catch errors early
Start simple, add complexity as needed
