Introduction to 4 Agentic AI Design Patterns
Kashif Manzoor
Enabling Customers for a Successful AI Adoption | AI Tech Evangelist | AI Solutions Architect
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Today at a Glance:?
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 delivery vehicle routes and schedules 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 handle varying materials and assembly processes efficiently.
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.
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.
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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 to perform designated tasks, improve their IT ecosystem, and interact intelligently with it.?
By leveraging these patterns, businesses can enhance efficiency, reduce operational costs, and improve service delivery across various domains.
Weekly News & Updates...
Last week's AI breakthroughs marked another leap forward in the tech revolution.
The Cloud:?the backbone?of the?AI revolution
Generative AI?Use Case of the Week:
Urban planning is evolving with the integration of Generative AI, offering innovative solutions to design challenges. Public Works Departments can now capitalize on the power of large language models (LLMs) to generate tailored urban design proposals for parks, housing layouts, and public spaces. These AI-driven suggestions streamline planning processes while aligning with community needs and sustainability goals.
To access the library of Gen AI Use cases,?link here:
Chief?AI Officer (CAIO) Corner:
Three tips for CAIOs
Favorite?Tip Of The Week:
Here's my favorite resource of the week.?
Using the DINOv2 open source model from Meta FAIR, EndoDINO, a foundation model that delivers SOTA performance across a range of GI endoscopy tasks, details to read over: How Virgo is using DINOv2 to analyze endoscopy videos for precision medicine.?link
Potential of AI
OpenAI has published an Economic Blueprint outlining policy proposals to maximize AI’s benefits, bolster national security, and drive economic growth in the United States. It discusses the importance of building a democratic AI ecosystem that supports entrepreneurship and personal freedom, ensuring AI development aligns with American values and safeguards. The full details of the proposals can be found on OpenAI’s?website. Below are the few examples presented in the report where it is being used.
Things to Know...
The UK’s Prime Minister has recently outlined an ambitious?blueprint?aimed at harnessing the power of AI for economic and social advancement. The plan emphasizes substantial investment in AI technology and infrastructure to position the UK as a leading AI innovator on the global stage. This includes enhancing public services through AI?
Applications, addressing safety risks associated with AI technologies, and establishing the UK as a prime destination for AI firms and talent. This strategic move no
t only aims to boost the national economy but also to ensure that AI advancements are safe and beneficial across various sectors
The Opportunity...
Podcast:?
Courses to attend:
Events:
Tech and Tools...
And that’s a wrap!
Thank you, as always, for taking the time to read.?
I’d love to hear your thoughts. Please reply and let me know what you find most valuable this week. Your feedback means a lot.
Until next week,?
Kashif Manzoor
The opinions expressed here are solely my conjecture based on experience, practice, and observation. They do not represent the thoughts, intentions, plans, or strategies of my current or previous employers or their clients/customers. The objective of this newsletter is to share and learn with the community.