---
title: "Agentic Design Patterns"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Agentic Design Patterns

[Design Patterns](https://www.linkedin.com/posts/rakeshgohel01_not-all-ai-agents-are-created-equally-here-activity-7276242517904302080-gsTJ?utm_source=share&utm_medium=member_desktop)

Agentic Design Patterns refer to recurring solutions or approaches used to structure autonomous AI agents. These patterns define how agents reason, act, and interact with their environment, other agents, or users to achieve specific goals. They are conceptual frameworks that guide the design and implementation of AI agents to maximize efficiency, adaptability, and performance in complex, dynamic environments.

The term "agentic" emphasizes the autonomy of these systems, meaning they can operate independently, make decisions, and perform actions without constant human intervention.

Here are some well-known **Agentic AI Design Patterns**:

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### 1. **ReACT (Reasoning and Acting)**
- **Overview**: Combines reasoning and acting in an iterative loop. The agent reasons about the task at hand, takes an action, and uses the result of that action to further refine its reasoning.
- **Example**: An agent planning a trip may alternately research flights (reasoning) and book tickets (acting), refining the next steps based on the availability and pricing of flights.
- **Strengths**: Highly effective for tasks requiring sequential decision-making with intermediate feedback.
- **Weaknesses**: Can get stuck in loops if reasoning fails to converge.

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### 2. **Self-Improvement**
- **Overview**: The agent continuously evaluates and enhances its own capabilities, often by retraining, learning from new data, or optimizing its internal processes.
- **Example**: A coding assistant that improves its suggestions by analyzing feedback from users or training on new programming datasets.
- **Strengths**: Adaptive to new challenges and improves over time.
- **Weaknesses**: Requires substantial computational resources and careful monitoring to avoid unintended behaviors.

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### 3. **Agentic RAG (Retrieval-Augmented Generation)**
- **Overview**: Combines a retrieval system (e.g., a database or search engine) with a generative AI model. The agent retrieves relevant data and uses it to generate responses or take actions.
- **Example**: A customer support chatbot retrieves policy documents and crafts personalized responses to user inquiries.
- **Strengths**: Enhances generative AI with factual grounding from external sources.
- **Weaknesses**: Depends on the quality and accuracy of the retrieved information.

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### 4. **Meta-Agent**
- **Overview**: An overarching agent that coordinates or manages multiple sub-agents, each specialized in a specific task.
- **Example**: A project manager AI that delegates tasks to scheduling, budgeting, and reporting agents, ensuring they work harmoniously.
- **Strengths**: Scalable and modular, allowing for specialization.
- **Weaknesses**: Requires robust coordination and communication mechanisms.

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### 5. **Planner-Executor**
- **Overview**: Separates the agent into two distinct roles: a planner that devises strategies and an executor that implements them.
- **Example**: A game AI plans a sequence of moves to win (planner) and executes them in the game environment (executor).
- **Strengths**: Clear separation of concerns improves performance and modularity.
- **Weaknesses**: Inefficient if plans are frequently disrupted and require re-planning.

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### 6. **Reflexive Agent**
- **Overview**: Operates on a stimulus-response model, where the agent immediately reacts to changes in its environment without extensive reasoning.
- **Example**: A robotic vacuum that changes direction upon detecting an obstacle.
- **Strengths**: Fast and efficient for real-time tasks.
- **Weaknesses**: Limited adaptability to complex or unforeseen scenarios.

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### 7. **Interactive Learning**
- **Overview**: The agent learns by interacting with users, collecting feedback, and adjusting its behavior accordingly.
- **Example**: A language model that improves its responses based on corrections provided by users.
- **Strengths**: Engages users in the learning process, making the agent more tailored to their needs.
- **Weaknesses**: Dependent on user input quality and volume.

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### 8. **Hierarchical Task Decomposition**
- **Overview**: Breaks down complex tasks into smaller, manageable subtasks, often using a hierarchical structure.
- **Example**: An AI assistant tasked with organizing an event divides the task into venue booking, invitation sending, and schedule creation.
- **Strengths**: Handles complex, multi-step tasks efficiently.
- **Weaknesses**: Requires accurate task breakdown and prioritization.

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### 9. **Goal-Oriented Agent**
- **Overview**: Operates by setting, pursuing, and refining goals, ensuring all actions align with achieving the defined objectives.
- **Example**: A financial planning AI that adjusts investment strategies to achieve specific savings goals.
- **Strengths**: Ensures focused and purposeful actions.
- **Weaknesses**: Can struggle with ambiguous or conflicting goals.

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### 10. **Contextual Memory**
- **Overview**: Leverages memory to store past interactions, using this information to enhance future decisions and responses.
- **Example**: A conversational agent remembers user preferences across sessions and tailors interactions accordingly.
- **Strengths**: Builds continuity and personalization.
- **Weaknesses**: Requires robust data management to avoid errors or inefficiencies.

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### 11. **Collaborative Multi-Agent Systems**
- **Overview**: Multiple agents work together, each specializing in a subset of tasks, to achieve a shared goal.
- **Example**: Autonomous drones coordinating to deliver packages in a city.
- **Strengths**: Distributed workload and parallel task completion.
- **Weaknesses**: Requires effective communication and conflict resolution mechanisms.

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### 12. **Exploratory Agent**
- **Overview**: Focuses on exploring unknown environments or datasets to gather new information and insights.
- **Example**: A research assistant AI that scans academic journals to identify emerging trends.
- **Strengths**: Useful for discovering novel solutions or opportunities.
- **Weaknesses**: Risk of wasting resources on irrelevant exploration.

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### 13. **Adaptive Workflow Orchestration**
- **Overview**: Dynamically adjusts workflows based on changing priorities, resources, or environments.
- **Example**: An AI system managing hospital operations, reallocating resources based on patient influx.
- **Strengths**: Flexible and responsive to change.
- **Weaknesses**: Computationally intensive and prone to errors in highly chaotic scenarios.

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### 14. **Self-Healing Systems**
- **Overview**: Identifies and corrects its own errors or failures to maintain operational integrity.
- **Example**: A cloud management agent that detects and fixes failing nodes in a distributed system.
- **Strengths**: Increases reliability and reduces downtime.
- **Weaknesses**: Complex to implement and debug.

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### 15. **Ethical Decision-Making**
- **Overview**: Integrates ethical considerations into decision-making processes, often balancing competing priorities.
- **Example**: An autonomous vehicle deciding between two collision scenarios based on ethical principles.
- **Strengths**: Aligns AI behavior with societal values and norms.
- **Weaknesses**: Ambiguity in ethical frameworks can lead to inconsistent behavior.

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