In the ever-evolving landscape of agentic AI workflows and applications, understanding and leveraging design patterns is crucial for building effective and innovative solutions. Agentic AI design patterns provide structured approaches to solving complex problems. They enhance the capabilities of AI agents by enabling reasoning, planning, collaboration, and tool integration.
For instance, you can think of these patterns as a blueprint for constructing a well-oiled team of specialists in a workplace—each with unique roles and tools, working in harmony to tackle a project efficiently and innovatively. Imagine a team of engineers collaborating on designing a new car, where one member focuses on aerodynamics, another on engine performance, and a third on safety features, all using specialized tools to achieve a unified goal.
This blogpost explores some of the key design patterns suggested by Andrew NG, shedding light on their practical applications and benefits (Watch Here). Whether you are an AI researcher refining models with iterative feedback, a product manager streamlining operations and enhancing user experiences, or a developer building robust applications, these patterns offer valuable insights and strategies to elevate your projects.
Key Design Patterns and Real-World Examples
1. Reflection with LLMs
This pattern involves collaborating with LLMs to refine their output instead of relying on zero-shot responses. Zero-shot responses occur when AI generates an answer without prior examples or context-specific training, often resulting in less precise outputs. By iterating with the AI, users can guide the system to produce results that better align with their needs.
Example: In content creation, an LLM generates a draft blog post. The user iteratively provides feedback and prompts for improvements, refining the tone, accuracy, and content until the desired output is achieved, akin to a human-editor loop.
2. Tools Use
This pattern enables LLM agents to use external tools to perform actions. Examples include:
- Customer Service: An AI agent integrates with a company’s CRM to retrieve or update customer data.
- Healthcare: An AI-driven assistant accesses an external medical database to assist doctors with diagnoses and treatment options.
- Task Automation: An AI agent uses a calendar application to schedule meetings.
These patterns involve APIs for integration, function calls for executing tasks programmatically, and writing to databases for consistent and accessible data storage. Together, they enhance the versatility and functionality of AI systems across domains like healthcare, education, and logistics.
3. Reasoning / Planning
This pattern leverages LLMs to determine a sequence of steps or actions to achieve a goal.
Example: An AI travel assistant plans a vacation itinerary, identifying the optimal sequence of flights, accommodations, and activities based on user preferences, constraints, and real-time data.
4. Multi-agent Collaboration
Multiple agents work together to achieve a design goal.
Example: In supply chain management, one AI agent predicts demand, another optimizes inventory, and a third coordinates logistics. This collaboration ensures efficiency and cost-effectiveness across the supply chain.
5. Agents Having Single Responsibility
Experts recommend creating AI agents with a single focus to ensure high accuracy and reliability in their tasks.
Examples:
- Sentiment Analysis: An AI agent classifies customer support tickets as positive, negative, or neutral.
- Incident Prioritization: An agent assigns priorities such as “critical,” “high,” or “low” to customer issues.
- Email Automation: An agent sends notifications, such as confirming ticket updates or delivering resolution summaries.
Such narrowly focused agents are easier to manage and integrate into larger workflows for customer service management.
Why These Design Patterns Matter
By incorporating these design patterns, product managers, architects, and developers can create sophisticated and capable agentic AI systems to address complex challenges.
Impactful Industries:
- Healthcare: AI streamlines diagnosis and treatment planning.
- Logistics: Multi-agent systems optimize supply chains.
- Customer Service: Sentiment analysis and prioritization enhance user experiences.
By targeting specific challenges like operational efficiency and data-driven decision-making, these design patterns pave the way for transformative advancements across diverse fields.
Additional Resources
For readers interested in diving deeper, we have curated a comprehensive list of agentic AI resources, including research papers, videos, and courses. Explore it here: Agentic AI Resources.
These patterns streamline workflows, enhance decision-making, and enable seamless integration of AI with external tools and multi-agent systems, driving innovation and efficiency across industries.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
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I found it very helpful. However the differences are not too understandable for me