Design Patterns for AI Agent Systems

Foundation: Basic Architecture Patterns

Simple Model

When to use:

• Tasks require only text generation
• No external actions or tool usage needed
• Direct question-answer scenarios from training data
• Document summarization or code generation from specifications

Characteristics:

• Fast and cost-effective
• Easy to implement with direct LLM API calls
• Limited to text processing, analysis, or generation

Example Use Cases:

• Text completion and generation
• Simple question answering
• Content summarization
• Code generation from clear specifications

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Single Agent

When to use:

• Tasks require actions beyond text generation
• Solution involves known action sequences with action-perception loops
• Can be handled effectively by one type of expertise
• Tasks involve API calls with result validation
• No need for specialized domain expertise or complex collaboration

Characteristics:

• Flexible with tool usage capabilities
• Moderate complexity
• Can handle action-taking with appropriate tools
• May struggle with highly complex multi-step tasks

Example Use Cases:

• Simple automation tasks
• Basic tool calling scenarios
• Straightforward decision-making
• Tasks requiring validation loops

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Multi-Agent Workflow

When to use:

• Solution can be expressed as a well-defined workflow
• Benefits from specialized expertise or domain separation
• Different parts of the task require distinct domain knowledge
• Collaboration pattern between agents is well-understood
• Each agent's role and handoffs follow established processes

Characteristics:

• Structured and maintainable
• Clear agent responsibilities
• Orchestrated, predictable execution flow
• Requires upfront design
• Less flexible than autonomous approaches

Example Use Cases:

• Content creation pipeline (research, writing, editing agents)
• Customer service routing (triage, specialist, resolution)
• Financial analysis (market data, risk assessment, portfolio optimization)
• Data processing workflows with specialized steps

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Workflow Patterns (Explicit Control)

Workflow patterns provide developer-defined execution paths with predictable behavior. These patterns borrow concepts from graph theory to model multi-agent orchestration as computational graphs where nodes represent computational units and edges define control flow between nodes.

Sequential Workflows

Sequential workflows implement linear execution where each node's output feeds into the next node (A → B → C), ensuring ordered processing with predictable execution timing.

flowchart LR
    User[("User<br/>Request")]
    A["A: Topic Selection<br/>────────<br/>Identifies key topics<br/>from user input"]
    B["B: Research<br/>────────<br/>Gathers information<br/>on selected topics"]
    C["C: Analysis<br/>────────<br/>Processes and<br/>analyzes research data"]
    D["D: Report Generation<br/>────────<br/>Creates comprehensive<br/>final report"]
    End([Output])
    
    User -.->|Input| A
    A -->|Selected Topics| B
    B -->|Research Data| C
    C -->|Analysis Results| D
    D -.->|Final Report| End
    
    style User fill:#004987,stroke:#003665,stroke-width:2px,color:#fff
    style A fill:#259638,stroke:#1C712A,stroke-width:3px,color:#fff
    style B fill:#632C4F,stroke:#4E223E,stroke-width:3px,color:#fff
    style C fill:#853175,stroke:#6A275E,stroke-width:3px,color:#fff
    style D fill:#057398,stroke:#045672,stroke-width:3px,color:#fff
    style End fill:#259638,stroke:#1C712A,stroke-width:2px,color:#fff

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Conditional Workflows

Conditional workflows use logic-based edges to determine the next node based on conditions, enabling branching execution paths and dynamic routing.

flowchart LR
    User[("User<br/>Request")]
    A["A: Write Code<br/>────────<br/>Generates code based<br/>on requirements"]
    B["B: Test Code<br/>────────<br/>Runs tests and<br/>validates functionality"]
    D["D: Deploy Code<br/>────────<br/>Deploys to<br/>production"]
    End([Success])
    
    User -.->|Requirements| A
    A -->|Code| B
    B -->|Pass| D
    B -.->|Fail| A
    D -.->|Deployed| End
    
    style User fill:#004987,stroke:#003665,stroke-width:2px,color:#fff
    style A fill:#259638,stroke:#1C712A,stroke-width:3px,color:#fff
    style B fill:#853175,stroke:#6A275E,stroke-width:3px,color:#fff
    style D fill:#632C4F,stroke:#4E223E,stroke-width:3px,color:#fff
    style End fill:#259638,stroke:#1C712A,stroke-width:2px,color:#fff

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Supervisor Workflows

A supervisor workflow is a conditional workflow variant where a central control node evaluates requests and routes tasks to specialized agents based on task characteristics.

flowchart TD
    User[("User<br/>Inquiry")]
    Supervisor["Supervisor<br/>────────<br/>Evaluates inquiry type<br/>and routes to specialist"]
    Tech["Technical Support<br/>────────<br/>Handles technical<br/>issues"]
    Billing["Billing Support<br/>────────<br/>Handles payment<br/>and billing"]
    Sales["Sales Team<br/>────────<br/>Handles product<br/>inquiries"]
    End([Response])
    
    User -.->|Inquiry| Supervisor
    Supervisor -->|Technical Issue| Tech
    Supervisor -->|Billing Question| Billing
    Supervisor -->|Product Info| Sales
    Tech -.-> End
    Billing -.-> End
    Sales -.-> End
    
    style User fill:#004987,stroke:#003665,stroke-width:2px,color:#fff
    style Supervisor fill:#9E57A2,stroke:#7E4582,stroke-width:3px,color:#fff
    style Tech fill:#259638,stroke:#1C712A,stroke-width:3px,color:#fff
    style Billing fill:#632C4F,stroke:#4E223E,stroke-width:3px,color:#fff
    style Sales fill:#853175,stroke:#6A275E,stroke-width:3px,color:#fff
    style End fill:#259638,stroke:#1C712A,stroke-width:2px,color:#fff

---

Hierarchical Workflows

flowchart TD
    User[("User<br/>Request")]
    Main["M: Main Supervisor<br/>────────<br/>Routes to appropriate<br/>department"]
    
    TechSup["T: Tech Supervisor<br/>────────<br/>Manages technical<br/>support team"]
    BillSup["B: Billing Supervisor<br/>────────<br/>Manages billing<br/>support team"]
    
    L1["L1: Level 1 Support<br/>────────<br/>First-line<br/>technical support"]
    L2["L2: Level 2 Support<br/>────────<br/>Advanced<br/>technical support"]
    
    Payment["P: Payment Team<br/>────────<br/>Handles payment<br/>processing"]
    Refund["R: Refund Team<br/>────────<br/>Handles refund<br/>requests"]
    
    End1([Resolution])
    End2([Resolution])
    End3([Resolution])
    End4([Resolution])
    
    User -.->|Request| Main
    Main -->|Technical| TechSup
    Main -->|Billing| BillSup
    
    TechSup -->|Basic| L1
    TechSup -->|Advanced| L2
    
    BillSup -->|Payment| Payment
    BillSup -->|Refund| Refund
    
    L1 -.-> End1
    L2 -.-> End2
    Payment -.-> End3
    Refund -.-> End4
    
    style User fill:#004987,stroke:#003665,stroke-width:2px,color:#fff
    style Main fill:#9E57A2,stroke:#7E4582,stroke-width:4px,color:#fff
    style TechSup fill:#632C4F,stroke:#4E223E,stroke-width:3px,color:#fff
    style BillSup fill:#853175,stroke:#6A275E,stroke-width:3px,color:#fff
    style L1 fill:#259638,stroke:#1C712A,stroke-width:2px,color:#fff
    style L2 fill:#057398,stroke:#045672,stroke-width:2px,color:#fff
    style Payment fill:#259638,stroke:#1C712A,stroke-width:2px,color:#fff
    style Refund fill:#057398,stroke:#045672,stroke-width:2px,color:#fff
    style End1 fill:#259638,stroke:#1C712A,stroke-width:2px,color:#fff
    style End2 fill:#259638,stroke:#1C712A,stroke-width:2px,color:#fff
    style End3 fill:#259638,stroke:#1C712A,stroke-width:2px,color:#fff
    style End4 fill:#259638,stroke:#1C712A,stroke-width:2px,color:#fff

---

Parallel Workflows

flowchart TD
    User[("User<br/>Request")]
    Split["Split Request<br/>────────<br/>Distributes work<br/>to parallel agents"]
    
    A1["Agent 1<br/>────────<br/>Process Part A"]
    A2["Agent 2<br/>────────<br/>Process Part B"]
    A3["Agent 3<br/>────────<br/>Process Part C"]
    
    Merge["Merge Results<br/>────────<br/>Combines outputs<br/>into final result"]
    End([Output])
    
    User -.->|Input| Split
    Split -->|Part A| A1
    Split -->|Part B| A2
    Split -->|Part C| A3
    
    A1 -->|Result A| Merge
    A2 -->|Result B| Merge
    A3 -->|Result C| Merge
    
    Merge -.->|Final Output| End
    
    style User fill:#004987,stroke:#003665,stroke-width:2px,color:#fff
    style Split fill:#9E57A2,stroke:#7E4582,stroke-width:3px,color:#fff
    style A1 fill:#259638,stroke:#1C712A,stroke-width:3px,color:#fff
    style A2 fill:#632C4F,stroke:#4E223E,stroke-width:3px,color:#fff
    style A3 fill:#853175,stroke:#6A275E,stroke-width:3px,color:#fff
    style Merge fill:#057398,stroke:#045672,stroke-width:3px,color:#fff
    style End fill:#259638,stroke:#1C712A,stroke-width:2px,color:#fff

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Autonomous Patterns (Emergent Control)

Autonomous patterns enable runtime-determined execution based on task state and agent reasoning. The critical concept is that the flow of control is driven by an AI model and dynamically determined at runtime. Rather than following prescribed paths, agents orchestrate through communication and shared understanding of the current task context.

These patterns exist on a spectrum of control, from structured orchestration to fully emergent behavior.

Plan-Based Orchestration Pattern

Handoff Pattern

graph TD
    User[User Request] --> Master[Master Agent Orchestrator]
    
    Master -->|Route to specialist| Specialist1[Specialist Agent 1]
    Master -->|Route to specialist| Specialist2[Specialist Agent 2]
    Master -->|Route to specialist| Specialist3[Specialist Agent 3]
    
    Specialist1 -->|Hand off if needed| Specialist2
    Specialist2 -->|Hand off if needed| Specialist3
    Specialist3 -->|Hand off if needed| Specialist1
    
    Specialist1 -->|Return result| Master
    Specialist2 -->|Return result| Master
    Specialist3 -->|Return result| Master
    
    Master --> Response[Response to User]
    
    style User fill:#E6F1F5,stroke:#004987,color:#000
    style Master fill:#57C0E8,stroke:#853175,color:#000
    style Specialist1 fill:#C8E5CD,stroke:#259638,color:#000
    style Specialist2 fill:#81B8CB,stroke:#057398,color:#000
    style Specialist3 fill:#9E57A2,stroke:#632C4F,color:#000
    style Response fill:#C0DCE5,stroke:#632C4F,color:#000

---

Conversation-Driven Pattern (AI-Driven Conversation Pattern)

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Pattern Selection Criteria

Based on Task Characteristics:

• Well-defined, repeatable processes → Workflow patterns (Sequential, Conditional, Parallel)
• Dynamic, exploratory tasks → Autonomous patterns (Conversation-driven)
• Complex planning required → Plan-Based Orchestration
• Domain expertise needed → Handoff patterns
• Emergent solutions required → AI-Driven Conversation

Based on System Requirements:

• High predictability needed → Workflow patterns
• Maximum autonomy required → AI-Driven Conversation
• Resource constraints → Handoff patterns (minimal coordination overhead)
• Scalability concerns → Parallel Workflows or Handoff patterns
• Transparency required → Conversation-driven patterns (shared visibility)

Based on Implementation Considerations:

• Developer resources available → Workflow patterns (explicit control)
• Rapid prototyping needed → Conversation-driven patterns (simple implementation)
• Production reliability critical → Workflow patterns with explicit task management
• Human oversight required → Any pattern with human-in-the-loop integration
• Complex task decomposition → Plan-Based Orchestration

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References

• Microsoft: Multi-Agent Design Patterns
• Anthropic: Building Effective Agents
• LangGraph Design Patterns
• AutoGen Agent Patterns
• Semantic Kernel Orchestration