What is the Agentic Loop?

The agentic loop borrows from military strategy’s OODA loop (Observe, Orient, Decide, Act), popularized by John Boyd, but adapted for AI. In AI contexts, it’s the iterative cycle that transforms passive language models into proactive agents. Here’s a high-level breakdown:

•  Observe: Gather data from the environment, user inputs, or external sources.

•  Orient/Reason: Analyze the data, recall past knowledge, and plan the next step.

•  Decide/Plan: Choose actions or tools to use.

•  Act: Execute the chosen actions, which might involve calling APIs, querying databases, or interacting with the world.

•  Reflect/Loop: Evaluate results, update internal state, and repeat if needed.

This loop allows agents to handle tasks that require multiple steps, error correction, and adaptation—think of an AI booking a flight, debugging code, or managing a supply chain. As AI models like GPT-4 or Grok advance, the loop becomes more efficient, reducing hallucinations and improving reliability. The shift toward agentic AI is evident in frameworks like LangGraph, which model these loops as graphs for better visualization and debugging.

Why does this matter? In 2025, with tools like the Gemini API, developers can bootstrap simple agents in Python, as seen in educational courses on platforms like freeCodeCamp. But to scale, we need design patterns—reusable blueprints that address common challenges.

Simple Design Patterns: Getting Started with the Basics

Let’s begin with foundational patterns that implement the agentic loop in straightforward ways. These are ideal for beginners or applications with limited complexity, focusing on core iteration without heavy overhead.

1. The ReAct Pattern (Reason + Act)

The ReAct (Reasoning + Acting) pattern is one of the simplest yet most powerful ways to structure an agentic loop. Introduced in research papers, it prompts the AI to alternate between thinking (reasoning) and doing (acting via tools).

•  How it Works: The agent receives a task, reasons about it (“What do I know? What do I need?”), decides on an action (e.g., “Search the web for data”), executes it, observes the result, and reasons again. This loops until resolution.

•  Example: An AI research assistant. User asks: “What’s the latest on quantum computing?” The agent reasons: “I need current news.” Acts: Calls a search API. Observes: Processes results. Reasons: “Summarize key points.” If incomplete, loops back.

•  Pros: Easy to implement with libraries like LangChain or AutoGen. Handles tool calling seamlessly.

•  Cons: Can get stuck in infinite loops without safeguards; lacks deep planning.

•  When to Use: For tasks like question-answering with external tools, where iteration is short.

In practice, ReAct powers many chatbots today, evolving them from static responders to dynamic problem-solvers.

2. Tool Use Pattern

Building on ReAct, the Tool Use pattern emphasizes equipping agents with external “tools” (functions or APIs) to extend their capabilities beyond text generation.

•  How it Works: In the loop, the agent decides when to invoke tools (e.g., a calculator for math, a browser for real-time info). After acting, it incorporates the output into the next reasoning step.

•  Example: A coding agent like those in Vercel’s ecosystem. It reasons about a bug, acts by running code in a sandbox, observes errors, and iterates.

•  Pros: Transforms advisors into operators—e.g., from suggesting code to executing it.

•  Cons: Tool reliability is crucial; failures can break the loop.

•  When to Use: For operational tasks like data analysis or automation, where the AI needs to interact with the real world.

This pattern is foundational in platforms like Azure’s Agent Factory, where agents evolve from advisors to executors.

Intermediate Design Patterns: Adding Depth and Reliability

As tasks grow more complex, simple loops need enhancements like memory and self-correction. These patterns introduce reflection and planning to make agents more robust.

3. Reflection Pattern

The Reflection pattern adds a self-improvement layer, where the agent critiques its own outputs within the loop.

•  How it Works: After acting, the agent reflects: “Was this accurate? What went wrong?” It generates feedback, refines its approach, and loops back.

•  Example: A writing agent drafts an article, reflects on clarity and coherence, revises, and repeats until satisfied.

•  Pros: Improves reliability by reducing errors over iterations; mimics human learning.

•  Cons: Increases computation costs due to extra LLM calls.

•  When to Use: For creative or precision tasks like content generation or debugging, where quality matters.

Frameworks like CrewAI simplify this by baking reflection into agent workflows.

4. Planning Pattern

For multi-step problems, the Planning pattern decomposes tasks into sub-goals before looping.

•  How it Works: The agent first creates a plan (e.g., a tree of steps), then executes in a loop, adjusting based on observations.

•  Example: Trip planning agent: Plans “Search flights > Book hotel > Arrange transport,” acts on each, reflects on availability, and replans if needed.

•  Pros: Handles complexity by breaking it down; integrates with patterns like Tree of Thoughts.

•  Cons: Over-planning can lead to rigidity; requires strong reasoning models.

•  When to Use: For goal-oriented tasks like project management or research.

Google Cloud’s loop agent pattern excels here for iterative workflows in enterprise settings.

Advanced Design Patterns: Scaling to Complexity

For cutting-edge applications, we move to patterns involving multiple agents, adaptation, and integration with broader systems.

5. Multi-Agent Workflow

This pattern orchestrates multiple specialized agents in a collaborative loop.

•  How it Works: Agents divide labor (e.g., one researches, another analyzes, a third executes), communicating via shared memory or messages. The overall loop coordinates them.

•  Example: A software development team: Coder agent writes code, Tester agent runs tests, Reviewer agent reflects—all looping until the product is ready.

•  Pros: Scales to enterprise levels; leverages specialization for efficiency.

•  Cons: Communication overhead; potential for conflicts.

•  When to Use: In simulations, R&D, or large-scale automation, as seen in AWS’s agentic patterns.

6. Self-Improving and Resource-Aware Patterns

Advanced loops incorporate learning and optimization, like resource-aware routing (choosing models based on task complexity) or self-evolution.

•  How it Works: The agent tracks performance metrics, fine-tunes itself, or routes to better tools/models mid-loop.

•  Example: An e-commerce agent learns from user feedback, improving recommendations over time.

•  Pros: Adapts to changing environments; optimizes costs.

•  Cons: Requires monitoring infrastructure.

•  When to Use: Long-running systems like chatbots or IoT controllers.

Patterns like CodeAct extend this by letting agents generate and execute code dynamically.

7. Hierarchical Agent Pattern

This pattern structures agents in a layered hierarchy, where higher-level agents oversee and delegate to lower-level ones, enabling scalable decision-making.

•  How it Works: A top-tier “supervisor” agent breaks down high-level goals into sub-tasks, assigns them to specialized sub-agents, collects results, and iterates the loop at multiple levels. This allows for nested loops within the overall cycle.

•  Example: In a virtual enterprise simulation, a CEO agent sets strategic goals, delegates to department-head agents (e.g., marketing, finance), who further delegate to worker agents. The hierarchy reflects on outcomes and adjusts strategies upward.

•  Pros: Manages extreme complexity by distributing cognition; promotes modularity and reusability.

•  Cons: Can introduce latency from inter-layer communication; requires careful design to avoid bottlenecks.

•  When to Use: For large-scale systems like autonomous organizations or complex simulations, where tasks span multiple domains and require oversight.

8. Human-in-the-Loop Pattern

This pattern integrates human oversight into the agentic loop for enhanced safety, ethics, and accuracy in high-stakes scenarios.

•  How it Works: The agent pauses at critical decision points to seek human input, incorporates the feedback into its reflection step, and continues the loop. This can be triggered by confidence thresholds or predefined rules.

•  Example: A medical diagnostic agent analyzes symptoms and proposes treatments, but halts to present options to a doctor for approval or modification before acting (e.g., recommending tests). The loop resumes with the refined plan.

•  Pros: Mitigates risks like errors in sensitive areas; combines AI efficiency with human judgment.

•  Cons: Slows down the process; depends on human availability.

•  When to Use: In regulated fields like healthcare, finance, or legal advice, where accountability is paramount.

Getting Started with Pydantic AI: A Great Entry Point for Building Agents

Before wrapping up, let’s spotlight Pydantic AI, a powerful yet accessible framework for constructing AI agents. Built on the foundation of Pydantic—a popular Python library for data validation and settings management—Pydantic AI extends these capabilities to create type-safe, structured agents with ease. It’s my favorite tool for building out agents because it simplifies the process of defining tools, handling dynamic instructions, and ensuring structured outputs, all while maintaining modularity and scalability.

Why is Pydantic AI a great place to get started, especially for beginners?

•  Beginner-Friendly Setup: You can build a basic agent in just 10 minutes using simple Python code. Tutorials abound, guiding you through creating agents that integrate with LLMs like Grok or Gemini, without overwhelming complexity.

•  Type Safety and Structure: It leverages Pydantic’s models to enforce data types, reducing errors in agent inputs/outputs. This is crucial for reliable loops, as it prevents common pitfalls like mismatched data formats in tool calls or responses.

•  Tool Integration: Easily add custom tools (e.g., APIs for web search or data processing) with decorators, making it seamless to implement patterns like ReAct or Tool Use. Features like dependency injection allow agents to access external resources dynamically.

•  Scalability for Advanced Patterns: As you progress, it supports multi-agent workflows, reflection, and even hierarchical designs through composable components. It’s ideal for evolving from simple chatbots to sophisticated systems in domains like financial analytics or stock portfolio management.

•  Community and Resources: With documentation at ai.pydantic.dev, practical examples (e.g., CLI coding agents or full-stack apps), and integrations with frameworks like LangGraph, it’s backed by a growing ecosystem. Plus, it’s open-source, so you can experiment freely without vendor lock-in.

If you’re new to agentic AI, start with Pydantic AI—install it via pip, define your first agent class, and watch how quickly you can prototype an agentic loop. It’s the perfect bridge from theory to practice.

Conclusion: Embracing the Agentic Future

The agentic loop is more than a technical construct—it’s a paradigm shift toward AI that thinks and acts like us, but at scale. Starting from simple ReAct implementations to advanced hierarchical and human-integrated orchestrations, these design patterns provide a roadmap for building intelligent systems. As frameworks like LangGraph and AutoGen mature, expect even more innovation. Tools from xAI and others will further democratize this, but remember: Start simple, iterate, and always prioritize safety and ethics.

If you’re building agents, experiment with these patterns—perhaps using Python and free APIs, beginning with Pydantic AI as your foundation. The future is agentic; let’s loop into it thoughtfully.