As artificial intelligence continues to evolve, the shift from simple chatbot interfaces to truly autonomous, intelligent systems is becoming a reality. At the core of this transformation are agentic design patterns—reusable frameworks that help structure how AI agents plan, act, reflect, and collaborate.
These six design patterns are the backbone of today’s most advanced AI agent architectures, enabling smarter, more resilient systems.
1. ReAct Agent (Reasoning + Acting)
The ReAct pattern enables agents to alternate between reasoning through language and taking action via tools. Instead of passively responding to prompts, the agent breaks down tasks, reasons through steps, and uses external resources to achieve goals.
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Key feature: Thinks aloud and takes actions iteratively.
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Why it matters: Mimics human problem-solving and makes AI more interpretable and efficient.
2. CodeAct Agent
The CodeAct pattern focuses on enabling agents to write, execute, and debug code. This is especially useful for solving complex, technical problems or automating workflows that require logic and precision.
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Key feature: Dynamically generates and runs code in a live coding environment.
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Why it matters: Automates developer tasks and enables technical reasoning.
3. Modern Tool Use
This pattern teaches agents how to smartly select and utilize third-party tools (like APIs or internal services). The agent becomes a manager of digital resources, deciding when and how to delegate tasks to tools.
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Key feature: Picks the right tools based on task needs.
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Why it matters: Gives agents real-world utility without overcomplicating internal logic.
4. Self-Reflection
Self-reflection equips agents with a feedback loop. After completing a task or generating an answer, the agent evaluates the quality of its response, identifies potential errors, and revises accordingly.
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Key feature: Checks and improves its own output.
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Why it matters: Boosts reliability and encourages iterative learning.
5. Multi-Agent Workflow
Rather than a single monolithic agent, this pattern involves multiple specialized agents working together. Each one has a defined role (e.g., planner, coder, checker), and they communicate to solve problems collaboratively.
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Key feature: Division of labor between expert agents.
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Why it matters: Scales well for complex workflows and enhances performance.
6. Agentic RAG (Retrieval-Augmented Generation)
Agentic RAG combines external information retrieval with generative reasoning, memory, and tool use. It allows agents to pull in up-to-date or task-specific data to guide their decision-making and output.
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Key feature: Combines context-retrieval with deep reasoning.
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Why it matters: Provides grounded, accurate, and context-aware outputs.
Key Takeaway
These six agentic AI design patterns provide a strong foundation for building autonomous, context-aware systems that can reason, act, collaborate, and self-improve. As AI agents move deeper into industries from software development to customer service and beyond, these patterns will guide developers in designing robust, intelligent solutions that scale.
Whether you're building internal tools or next-generation AI applications, mastering these frameworks is essential for developing truly capable and autonomous agents.
References
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Marktechpost – “Top 6 Agentic AI Design Patterns”: https://aiagent.marktechpost.com/post/top-6-agentic-ai-design-patterns
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ReAct (Reasoning and Acting): https://arxiv.org/abs/2210.03629
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CodeAct examples (various GitHub and research projects; see pattern 2 details on link above)
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Agentic RAG concept: https://www.marktechpost.com/2024/02/15/openai-introduces-rag-chain-and-memory-management-using-gpt/
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Self-Reflection agent idea: https://arxiv.org/abs/2302.03432
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Multi-Agent Collaboration: https://arxiv.org/abs/2303.12712
