Design Patterns for LLM Agent Systems

Imagine a team of AI agents collaborating seamlessly: one researches the latest market trends, another analyzes the data, a third drafts a compelling report, and a fourth schedules its distribution. This coordinated action, powered by Large Language Model (LLM) agents, is transforming how we approach complex tasks. But how can we design these agents to interact effectively and achieve complex goals?

Unlike traditional AI systems, LLM agents can engage in sophisticated reasoning, planning, and interaction, making their coordination a rich area for innovation and design. Design patterns are essential for managing this complexity, ensuring efficient interactions, promoting reusability, and building robust and adaptable agent systems. Let’s explore the key patterns and capabilities that make effective agent collaboration possible.

Core Design Patterns

Sequential Flow

Sequential flow patterns organize agents in a predefined linear sequence, like an assembly line. Each agent’s output becomes input for the next, creating a clear chain of responsibility. This pattern excels in straightforward, step-by-step processes but may struggle with dynamic tasks requiring flexibility.

Example: Content Creation Pipeline

Research Agent
└── Gathers and synthesizes source material
Writing Agent
└── Creates initial draft
Editor Agent
└── Refines and polishes content
Publishing Agent
└── Formats and prepares final output

However, a key limitation is that a failure in any single stage can disrupt the entire workflow, requiring careful error handling and monitoring.

Sequential Flow Diagram

Planning, Execution, and Refinement

The Plan → Execute → Refine cycle is a common example of a more complex sequence. Let’s explore how an LLM agent approaches writing a blog post about solar energy benefits, demonstrating this cycle:

Solar Energy Blog Post Creation
1. Planning Phase
   ├── Topic Analysis
   │   ├── Core benefits identification
   │   ├── Target audience definition
   │   └── Key message points
   ├── Research Needs
   │   ├── Recent solar technology advances
   │   ├── Cost-benefit statistics
   │   └── Real-world implementation examples
   └── Quality Criteria
       ├── Technical accuracy
       ├── Reader engagement
       └── SEO optimization

2. Execution Phase
   ├── Research Collection
   │   ├── Scientific sources
   │   ├── Industry reports
   │   └── Case studies
   ├── Content Creation
   │   ├── Introduction hook
   │   ├── Benefits explanation
   │   └── Supporting evidence
   └── Format Application
       ├── Section headers
       ├── Visual suggestions
       └── Call-to-action

3. Refinement Phase
   ├── Quality Review
   │   ├── Fact verification
   │   ├── Flow assessment
   │   └── Engagement check
   ├── Feedback Integration
   │   ├── Expert reviews
   │   ├── SEO optimization
   │   └── Audience feedback
   └── Optimization
       ├── Content enhancement
       ├── Structure improvement
       └── Final polishing

The refinement phase involves comparing the draft against the defined quality criteria, incorporating feedback from users or other agents, and revising sections that need improvement. This could involve re-researching specific points, rephrasing sentences, or reorganizing the structure of the text. The agent can learn from this feedback, updating its internal models and improving its performance on future writing tasks.

Hierarchical Delegation

In hierarchical delegation, a master agent orchestrates the workflow by breaking down complex tasks and delegating to specialized worker agents. This pattern enables sophisticated project management but requires careful attention to prevent bottlenecks at the master agent level.

Example: Research Project Management

Project Manager Agent
├── Data Collection Agent
│   └── Web scraping, API queries, database access
├── Analysis Agent
│   └── Statistical analysis, pattern recognition
└── Reporting Agent
    └── Visualization, summary generation

Furthermore, the master agent can implement dynamic task allocation, assigning tasks based on worker availability, expertise, or current workload, optimizing resource utilization and system resilience.

Hierarchical Delegation Flow Diagram

Collaborative Model

The collaborative model enables agents to work together through a shared workspace or “blackboard.” This pattern supports flexible, asynchronous collaboration and emergent problem-solving, while requiring careful management of shared resources.

Example: Collaborative Problem-Solving

Shared Knowledge Base
├── Expert Agent A: Market Analysis
├── Expert Agent B: Technical Evaluation
├── Expert Agent C: Risk Assessment
└── Synthesis Agent: Final Recommendations

Collaborative Model Diagram

Mediated Collaboration

A mediator agent facilitates communication between multiple agents, ensuring organized interaction and preventing conflicts. This pattern provides structure but may introduce communication overhead.

Example: Expert Panel Discussion

Mediator Agent
├── Technical Expert
│   └── System architecture insights
├── Business Analyst
│   └── Market viability assessment
├── Legal Advisor
│   └── Compliance review
└── User Advocate
    └── Usability considerations

The mediator agent facilitates communication by managing turn-taking, routing messages between experts, resolving conflicts, and ensuring that the discussion stays focused and productive.

Mediated Collaboration Pattern Diagram

Combining Patterns for Enhanced Performance

Real-world systems often combine multiple patterns to leverage their respective strengths. Here’s an example of a sophisticated customer service system:

Support Manager Agent (Hierarchical)
├── Triage Agent (Sequential + Conditional)
│   ├── Issue Classification
│   └── Priority Assessment
├── Knowledge Base (Collaborative)
│   └── Shared solution repository
├── Specialist Agents (Role-Based)
│   ├── Technical Support
│   ├── Billing Support
│   └── Product Support
└── Quality Assurance Agent (Sequential)
    └── Response verification

This hybrid system combines:

• Hierarchical management for overall coordination
• Sequential processing for standard procedures
• Collaborative knowledge sharing among agents
• Role-based specialization for specific issues
• Adaptive routing based on issue classification

Benefits of this hybrid approach:

• Improved customer satisfaction through faster resolution and personalized support
• Reduced wait times by efficiently routing customers to the appropriate specialist agents
• Increased resolution rates due to access to a shared knowledge base and expert agents
• Enhanced scalability and flexibility in handling diverse customer inquiries

Exploring Key Capabilities and Considerations

These core patterns are enabled by several key capabilities and considerations, which we’ll explore next. Understanding how to implement these capabilities effectively is crucial for building successful agent systems.

Key Capabilities and Considerations

Tool Use and Integration

LLM agents extend their capabilities through function calling, enabling interaction with external tools, APIs, and services. This is frequently called “Tool Use” and it is directly supported by all the top LLMs. Effective tool integration requires careful design of the interface between agents and their tools.

Key considerations for tool integration:

Integration Framework
├── Function Specifications
│   └── Clear input/output definitions
├── Error Handling
│   └── Graceful failure recovery
├── Security Controls
│   └── Access management
├── Performance Monitoring
│   └── Resource usage tracking
└── Resource Management
    └── API rate limiting

For example, an agent could use a weather API to provide real-time weather updates within a conversation. The agent would send a structured request to the API, parse the response, and format the information into a natural language response for the user. Security and access control are paramount, ensuring that agents only have access to authorized tools and data, preventing unauthorized actions or data breaches.

Context and Memory

Context management is critical for LLM agent performance. The finite context window of LLMs requires careful optimization to maintain relevant information while managing costs.

Practical Strategies for Context Management:

1. Progressive Summarization Progressive summarization involves maintaining a condensed version of the conversation history. As new interactions occur, the agent identifies key information and updates the summary, discarding less relevant details while preserving the overall context. This allows the agent to retain important information from earlier turns even as the conversation progresses.

2. External Memory Integration

   Working Memory (Context Window)
   └── Active conversation
   External Memory
   ├── Conversation history
   │   └── Timestamped interaction logs
   │   └── Key decisions and outcomes
   ├── Reference documents
   │   └── Domain knowledge
   │   └── User preferences
   └── Previous decisions
       └── Solution patterns
       └── Error cases
   

   For example, a customer service agent could access previous interactions with a customer stored in external memory to provide personalized support and avoid asking redundant questions. Or, a research agent could retrieve relevant articles and papers from an external database based on the current research topic.

3. Context Prioritization

   Priority Levels
   ├── Highest: Current user input
   ├── High: Recent conversation turns
   ├── Medium: Conversation summary
   └── Low: Background information
   

   For example, in a technical support conversation, the user’s current error message might receive highest priority, followed by their recent troubleshooting steps, a summary of the issue’s history, and finally any relevant technical documentation or known issues from the knowledge base.

4. Memory Types

   ├── Short-term Memory
   │   ├── Handles immediate conversation context (current inputs/outputs).
   │   └── Limited by the LLM's context window size.
   ├── Medium-term Memory
   │   ├── Captures key interactions within a session.
   │   └── Enables continuity within the same session.
   └── Long-term Memory
       ├── Stores knowledge persistently across sessions.
       └── Supports personalization and historical learning.
   

Memory Types Diagram

Building Effective Agent Systems

Successful LLM agent implementations require careful consideration of:

1. Architecture Decisions
   • Pattern selection based on task requirements
   • Component integration strategy
   • Scalability planning
2. Resource Management
   • Context window optimization
   • Tool access coordination
   • Performance monitoring
3. Quality Control
   • Continuous validation
   • Error detection and handling
   • Output verification
4. Future Adaptability
   • Pattern combination flexibility
   • Capability extension
   • System evolution

The field of LLM agent design continues to evolve, offering exciting opportunities for innovation and improvement. By understanding and effectively implementing these patterns and capabilities, we can build increasingly sophisticated and capable agent systems that transform how we approach complex tasks.

By carefully addressing these considerations, developers can build LLM agent systems that are not only effective but also robust, ethically sound, and adaptable to future challenges.