Agentic AI Design Patterns
Last week, we covered how AI is evolving, and it is in the Agentic AI phase in 2025
Continuously to this topic, I got a question during this week’s session with a few tech engineers about whether there are common patterns for Agentic AI.
Before you go into details of the patterns, let’s have a look at the following:
What is Agentic AI
Agentic AI systems feature intelligent, independent agents collaborating to tackle complex problems. These agents surpass traditional AI, like just prompts or pre-defined tasks, by integrating advanced capabilities like “chaining,” enabling them to manage intricate, sequential tasks. They can perceive environments, make decisions, and learn from outcomes to achieve goals.
They have a spectrum of agent-like qualities and cover various systems and methodologies. Large language models (LLMs) are a key part of Agentic AI.
I worked on exploring these patterns during this week, and here are some common Agentic AI design patterns.
They describe how autonomous agents perceive, reason, communicate, and act in a multi-agent or distributed environment.
Each pattern has distinct benefits and trade-offs;
You can combine them to build more complex agentic systems.
Each pattern offers a way to structure decision-making, communication, and execution in complex scenarios.
Design Patterns for Agentic AI:
1 – Reflection:
The Reflection design pattern in Agentic AI involves the system’s ability to analyze its own performance and decision-making processes. This self-awareness allows the agent to adjust its behaviour based on past actions and outcomes, enhancing its effectiveness over time.
Use Cases: This pattern is particularly useful in dynamic environments where conditions change rapidly, such as in automated trading systems where an agent must evaluate its trading strategies and adapt to new market conditions without human intervention.
2 – Planning:
Planning is another design pattern in which the AI agent can foresee potential future states and devise a series of actions to achieve its goals. This involves complex problem-solving and decision-making processes based on predicted outcomes.
Use Cases: Planning is essential in logistics and supply chain management, where AI agents need to optimize routes and schedules for delivery vehicles based on traffic, weather conditions, and customer delivery windows.
3. Tool Use:
The tool-use design pattern enables AI agents to identify, select, and use tools or resources within their environment to accomplish specific tasks. This extends the agent’s capabilities beyond built-in functions to leveraging external tools or integrating with other systems.
Use Cases: In manufacturing, AI agents equipped with the Tool Use pattern can autonomously operate machinery, adjust parameters for different production runs, or switch between tools to efficiently handle varying materials and assembly processes.
4. Multi-Agent:
The multi-agent design pattern involves multiple AI agents working collaboratively to solve problems or complete tasks that are too complex for a single agent. This pattern focuses on coordination and cooperation among agents to optimize overall system performance.
Use Cases: Multi-agent systems are highly effective in smart city applications, such as coordinating traffic lights and public transportation schedules to optimize traffic flow and reduce congestion during peak times.
Generative artificial intelligence facilitates the development and implementation of agents. These agents can leverage distinguished reasoning and language processing capabilities to take a proactive, autonomous role in pursuing business process goals
Decision Tree
The paper “Agent Design Pattern Catalogue: A Collection of Architectural Patterns for Foundation Model-based Agents” covers 18 patterns. The best part is that you can decide which model you want to use below the decision tree to achieve a specific business task.
Call for Action
For organizations looking to implement Agentic AI, understanding these design patterns can support the development of robust AI systems that can operate autonomously and adapt to new challenges.
Each pattern provides a framework for designing AI agents that can perform designated tasks, improve their IT ecosystem, and intelligently interact with it.
By leveraging these patterns, businesses can enhance efficiency, reduce operational costs, and improve service delivery across various domains.
