1 project tagged with "design-patterns"

• AI Agent Design Patterns
  
  07-June-2026
  

  ReAct (Reason + Act)

  Concept: Alternate between reasoning steps and taking actions (tool calls, API calls, etc.)

  Flow:

  1. LLM generates reasoning / thought
  2. Take action (call a tool or external system)
  3. Observe output and feed it back into reasoning

  Strengths: Multi-step problem solving, tool orchestration, grounded responses

  Use case: Complex question answering, multi-tool agents, decision-making systems

  Tool-Calling / Tool-Augmented Agents

  Concept: The LLM acts as a controller that decides whether to call external tools

  Flow:

  1. LLM receives input
  2. LLM selects which tool(s) to call
  3. Execute tool(s)
  4. Synthesize final answer from tool output

  Strengths: Reduces hallucinations, improves grounding, enables use of APIs and external systems

  Use case: Wikipedia-style assistants, math solvers, structured data retrieval

  Reflex / Reactive Agents

  Concept: Immediate response without planning or multi-step reasoning

  Flow: Input → Response

  Strengths: Very fast, low complexity, low cost

  Use case: Chatbots, simple Q&A systems

  Plan-and-Execute / Hierarchical Planning Agents

  Concept: The agent first creates a plan, then executes steps sequentially

  Flow:

  1. LLM generates a plan
  2. Execute each step using tools or computations
  3. Return final result

  Strengths: Strong for complex workflows and multi-step tasks

  Use case: Automation systems, workflow orchestration, research agents

  Debate / Self-Reflection Agents

  Concept: Multiple candidate outputs are generated and evaluated before final selection

  Flow:

  1. Generate multiple candidate answers
  2. Critique or evaluate each candidate
  3. Select the best final output

  Strengths: Reduces errors, improves reliability, reduces hallucinations

  Use case: Code review, summarization, high-accuracy Q&A

  Memory-Augmented Agents

  Concept: The agent stores and retrieves long-term memory to maintain context

  Flow:

  1. Retrieve relevant past memory
  2. Reason using current input + memory
  3. Update memory with new information

  Strengths: Personalization, continuity, long-term context awareness

  Use case: Personal assistants, long-running agents, adaptive systems

  Summary
  • ReAct: reasoning + acting with tools in loops
  • Tool-Calling: LLM chooses external tools
  • Reflex: direct single-step response
  • Plan-and-Execute: plan first, then execute steps
  • Debate: multiple outputs + self-evaluation
  • Memory-Augmented: persistent context over time
  
  

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