Agentic Patterns: The Building Blocks of Reliable AI Agents

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Originally published on Towards AI.

Over the last few months, I’ve been diving deep into the world of AI agents. And honestly, it feels like the field is moving at the same pace as when deep learning first started to explode.

The idea is simple yet powerful: instead of static models that just return outputs, we can now build agents that can reason, act, and interact with the world. But once you start building these systems, you quickly realize it’s not enough to just connect a large language model (LLM) to a few APIs.

That’s where Agentic Patterns come in.

Just like software engineering has design patterns (Observer, Factory, Singleton…), the agent world is now developing its own set of reusable blueprints for building robust, reliable, and general-purpose agents.

In this blog, I’ll explain what these patterns are, why they matter, and show you some real examples. I’ll also share some references that helped me learn this faster.

Why Do We Even Need Agentic Patterns?

When I first built a basic chatbot, the structure looked like this:

User → Model → Response

Pretty straightforward.

But the moment I tried to extend this to something more useful, like an AI job scheduling agent (a side project I worked on recently) things got messy:

• The agent needed to reason about tasks before deciding.
• It had to call multiple APIs in sequence.
• It sometimes gave wrong answers, so it needed self-checks.
• Some parts of the workflow required specialized knowledge, not just general LLM reasoning.

This was the first time I realized:
Without structure, agents collapse under complexity.

That’s why researchers and builders started looking at recurring solutions, which became known as agentic patterns.

Think of them as the “engineering discipline” behind agents, helping us avoid reinventing the wheel every time.

Core Agentic Patterns

Let’s look at some of the most important patterns. I’ll use simple language, but we’ll also dive into how they’re used in real projects and research.

1. ReAct (Reason + Act)

Probably the most famous pattern is ReAct, introduced in a Google research paper (Yao et al., 2022).

The intuition is:

• LLMs don’t just spit out answers.
• They should reason step by step.
• And then act by calling tools, APIs, or performing actions.
• The results of those actions feed back into reasoning.

Example:

Let’s say you ask an agent: “Find me the cheapest flight from Mumbai to Paris next week and book it.”

• The agent first reasons:
  “I need flight options, then I need to select the cheapest, then I need to call the booking API.”
• It acts: calls the flight API.
• It observes the results.
• It reasons again:
  “Cheapest flight is X. Now I should call booking API with those details.”

This back-and-forth is much closer to human problem-solving than a single-shot output.

2. Self-Reflection Pattern

When I experimented with agents that write code (similar to Code Interpreter in ChatGPT), I noticed something:

• The model often generated wrong code on the first attempt.
• But if I manually asked it to “fix errors,” it usually succeeded.

That’s the reflection pattern: let the agent critique its own work.

In practice, it looks like:

1. Generate an initial answer.
2. Check it (e.g., run the code, validate facts, simulate the plan).
3. Reflect on what went wrong.
4. Improve the answer.

This is similar to how humans debug or edit their work.

Real-world Example:

• Reflexion framework (Shinn et al., 2023) uses reflection loops to drastically improve agent performance in reasoning tasks.
• GitHub Copilot-like tools also benefit from running generated code, seeing the error trace, and retrying.

3. Multi-Agent Collaboration

I’m personally very interested in this one because it feels closest to how real teams work.

Instead of building one super-agent that tries to do everything, you build multiple specialized agents that collaborate.

Example (Startup Idea Validation Crew):

• Idea Evaluator: judges feasibility.
• Customer Persona Builder: identifies target audience.
• MVP Recommender: suggests minimum viable product.

These agents can pass tasks to each other or work in parallel. Tools like CrewAI or LangGraph make this easier today.

Why this works:

• Each agent is simpler (narrow focus).
• Collaboration reduces errors.
• It’s modular; you can swap agents in/out.

4. Memory Patterns

This one is obvious but crucial. Agents without memory feel like talking to someone who forgets everything after 2 minutes.

Different kinds of memory exist:

• Short-term memory: stores recent conversation turns.
• Long-term memory: stores facts in a vector DB (e.g., Pinecone, Weaviate, Chroma).
• Episodic memory: remembers entire sessions (like “last time we solved algebra, you struggled with quadratic equations”).

Real-world Example:

• Personal AI assistants (like Mem.ai or PI) rely heavily on long-term memory.
• LangChain’s ConversationBufferMemory and VectorStoreRetrieverMemory implement these patterns in code.

Without memory, agents just can’t scale to real-life applications.

5. Critic-Helper Pattern

This one excites me because it mirrors human workflows, like one person creates and another reviews.

The pattern:

• Generator agent produces output.
• Critic agent checks correctness/quality.
• If flaws exist, generator retries.

Example:

• Generator writes a blog draft.
• Critic checks for factual accuracy.
• Generator revises based on feedback.

This is already used in alignment research (red-teaming LLMs) and can be applied in production for higher reliability.

How These Patterns Combine in Practice

In real projects, you don’t use just one pattern. You compose them.

For example, let’s say I’m building a customer support AI agent:

1. ReAct: To fetch product details step by step.
2. Reflection: To double-check answers against the knowledge base.
3. Memory: To remember user history and preferences.
4. Critic-Helper: To filter bad responses before showing them.
5. Multi-agent setup: One agent for billing, another for technical issues.

That’s when the system feels like a real assistant, not just a chatbot with fancy words.

References & Further Reading

• Yao et al. (2022). ReAct: Synergizing Reasoning and Acting in Language Models.
• Shinn et al. (2023). Reflexion: Language Agents with Verbal Reinforcement Learning.
• Park et al. (2023). Generative Agents: Interactive Simulacra of Human Behavior.
• LangGraph docs: LangGraph — Patterns for LLM Applications.
• CrewAI framework: CrewAI Website.

Final Thoughts

Agentic patterns are not just academic concepts, they’re becoming blueprints for the next generation of AI apps.

When I first started working with LLMs, I treated them like supercharged autocomplete engines. But as soon as I started layering patterns like reasoning, reflection, memory, and collaboration, the results became much more reliable.

If you’re building agents today, don’t just rely on prompts. Think in patterns.
That’s what separates toy demos from production-ready systems.

I’m curious, which of these patterns do you find most exciting? Should I write a dedicated deep dive (with code) on one of them next?

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Published via Towards AI