And let me say this upfront:
👉 You cannot build real AI Agents without understanding these 6 core patterns.
Here's my simplified, no-fluff breakdown:
Why Do Agentic Patterns Matter?
Patterns provide a structured way to think and design systems. They allow us to build and grow AI applications in complexity and adapt to changing requirements. Modular designs based on patterns are easier to modify and extend. Patterns help manage the complexity of coordinating multiple agents, tools, and workflows by offering proven, reusable templates. They promote best practices and shared understanding among developers.
When (and When Not) to Use Agents?
Before diving into patterns, it's crucial to consider when an agentic approach is truly necessary. Always seek the simplest solution first. If you know the exact steps required to solve a problem, a fixed workflow or even a simple script might be more efficient and reliable than an agent.
Agentic systems often trade increased latency and computational cost for potentially better performance on complex, ambiguous, or dynamic tasks. Be sure the benefits outweigh these costs.
- Use workflows for predictability and consistency when dealing with well-defined tasks where the steps are known.
- Use agents when flexibility, adaptability, and model-driven decision-making are needed.
- Keep it Simple: Even when building agentic systems, strive for the simplest effective design. Overly complex agents can become difficult to debug and manage.
- Agency introduces inherent unpredictability and potential errors. Agentic systems must incorporate robust error logging, exception handling, and retry mechanisms, allowing the system (or the underlying LLM) a chance to self-correct.
1. 𝗥𝗲𝗔𝗰𝘁 𝗔𝗴𝗲𝗻𝘁
➜ Thinks, takes action, looks at the result, repeats.
➜ Classic loop: "Should I Google this?" → Does it → Adjusts.
➜ Used in most AI products today (like basic chat assistants).
2. 𝗖𝗼𝗱𝗲𝗔𝗰𝘁 𝗔𝗴𝗲𝗻𝘁
➜ Runs real code, not just JSON.
➜ So instead of saying "call API X," it writes and runs a Python script.
➜ More powerful. Used in research agents and dev assistants.
3. 𝗠𝗼𝗱𝗲𝗿𝗻 𝗧𝗼𝗼𝗹 𝗨𝘀𝗲
➜ Sends tasks to smart APIs (search, cloud, data), and lets them do the heavy lifting.
➜ The agent mostly routes and formats info.
➜ Think: a smart middleman between you and powerful services.
4. 𝗦𝗲𝗹𝗳-𝗥𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻
➜ Agent checks its own work.
➜ Did it make a mistake? It catches it, critiques it, and tries again.
➜ Most AI errors happen because this step is missing.
5. 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄
➜ One agent isn't doing everything.
➜ You have a planner, a researcher, and a writer — all working together.
➜ Like a mini team of AIs. More accurate. Less chaos.
6. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚
➜ This is what powers apps like Perplexity.
➜ The agent looks stuff up (retrieval), thinks about it, uses tools, and gives you a smarter answer.
➜ Works with real-time data, not just model memory.
What Makes This Guide Different
📌 Implementation Details
- Code examples using pure API calls (Google Gemini)
- No framework dependencies (avoiding LangChain, LangGraph, etc.)
- Python implementation samples for each pattern
- Structured output schemas and JSON handling
📌 Use Cases & Applications
- Document generation and processing
- Code generation and debugging
- Customer support systems
- Research and report generation
- RAG implementations
- Multi-modal tasks
- Complex software development workflows
📌 Best Practices
- Combining and customizing patterns for real-world systems
- Empirical evaluation and performance measurement
- Avoiding over-engineering
- Error handling and retry mechanisms
This comprehensive guide covers everything you need to build robust AI agents from scratch, with practical examples and real-world applications.
Additional Workflow Patterns
In addition to the core agentic patterns, here are three common workflow patterns that provide structured approaches to building AI applications:
Workflow: Prompt Chaining
The output of one LLM call sequentially feeds into the input of the next LLM call. This pattern decomposes a task into a fixed sequence of steps. Each step is handled by an LLM call that processes the output from the preceding one. It's suitable for tasks that can be cleanly broken down into predictable, sequential subtasks.
Use Cases:
- Generating a structured document: LLM 1 creates an outline, LLM 2 validates the outline against criteria, LLM 3 writes the content based on the validated outline.
- Multi-step data processing: Extracting information, transforming it, and then summarizing it.
- Generating newsletters based on curated inputs.
Workflow: Routing or Handoff
An initial LLM acts as a router, classifying the user's input and directing it to the most appropriate specialized task or LLM. This pattern implements a separation of concerns and allows for optimizing individual downstream tasks (using specialized prompts, different models, or specific tools) in isolation. It improves efficiency and potentially reduces costs by using smaller models for simpler tasks.
Use Cases:
- Customer support systems: Routing queries to agents specialized in billing, technical support, or product information.
- Tiered LLM usage: Routing simple queries to faster, cheaper models and complex questions to more capable models.
- Content generation: Routing requests for blog posts, social media updates, or ad copy to different specialized prompts/models.
Workflow: Parallelization
A task is broken down into independent subtasks that are processed simultaneously by multiple LLMs, with their outputs being aggregated. This pattern uses concurrency for tasks. The initial query (or parts of it) is sent to multiple LLMs in parallel with individual prompts/goals. This can improve latency if subtasks don't depend on each other, or enhance quality through techniques like majority voting or generating diverse options.
Use Cases:
- RAG with query decomposition: Breaking a complex query into sub-queries, running retrievals for each in parallel, and synthesizing the results.
- Analyzing large documents: Dividing the document into sections, summarizing each section in parallel, and then combining the summaries.
- Generating multiple perspectives: Asking multiple LLMs the same question with different persona prompts and aggregating their responses.
- Map-reduce style operations on data.
Combining and Customizing Patterns
It's important to remember that these patterns aren't fixed rules but flexible building blocks. Real-world agentic systems often combine elements from multiple patterns. A Planning agent might use Tool Use, and its workers could employ Reflection. A Multi-Agent system might use Routing internally for task assignment.
The key to success with any LLM application, especially complex agentic systems, is empirical evaluation. Define metrics, measure performance, identify bottlenecks or failure points, and iterate on your design. Resist over-engineering.
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