Workflow and Agent Patterns for Intelligent Systems

Overview

This guide explores how intelligent systems can be structured using workflows (deterministic flows) and agents (autonomous decision-makers).
Workflows ensure reproducible outcomes, while agents enable adaptability, reasoning, and context awareness in dynamic environments.

Key Distinctions:

Workflows: Predetermined logic and fixed execution order.
Agents: Goal-driven, dynamic routing with tool and state awareness.

graph TD
    A[Input] --> B{Workflow or Agent?}
    B -->|Predetermined Path| C[Workflow]
    B -->|Dynamic Decision| D[Agent]
    
    C --> E[Sequential Steps]
    C --> F[Parallel Processing]
    C --> G[Conditional Routing]
    
    D --> H[Tool Selection]
    D --> I[Autonomous Decisions]
    D --> J[Continuous Feedback]
    
    E --> K[Predictable Output]
    F --> K
    G --> K
    H --> L[Adaptive Output]
    I --> L
    J --> L

LLM Augmentations

LLMs can be enhanced with structured reasoning, tool usage, and memory — transforming them from passive responders into dynamic reasoning engines capable of driving workflows or agentic behavior.

graph LR
    A[Base LLM] --> B[Tool Calling]
    A --> C[Structured Output]
    A --> D[Short-term Memory]
    
    B --> E[Enhanced LLM]
    C --> E
    D --> E
    
    E --> F[Workflows]
    E --> G[Agents]

1. Prompt Chaining Pattern

Used for deterministic multi-step tasks where each stage refines or validates the previous output — ensuring controlled, progressive improvement.

graph TD
    START --> A[Generate Initial Content]
    A --> B{Quality Check}
    B -->|Pass| END
    B -->|Fail| C[Improve Content]
    C --> D[Polish Content]
    D --> END

Highlights: Sequential flow with feedback gates and quality validation loops. Useful for iterative content, code, or data enrichment pipelines.

2. Parallelization Pattern

Ideal for tasks that are independent and can be executed simultaneously to improve performance and scalability.

graph TD
    START --> A[Task Dispatcher]
    A --> B[Subtask 1]
    A --> C[Subtask 2] 
    A --> D[Subtask 3]
    B --> E[Result Aggregator]
    C --> E
    D --> E
    E --> END

Highlights: Enables concurrency by distributing workloads and merging partial outcomes. Effective for large-scale document processing or data analysis.

3. Routing Pattern

A decision-based pattern where inputs are classified and directed to specialized branches, ensuring the right logic handles each case.

graph TD
    START --> A[Input Analyzer]
    A --> B{Route Decision}
    B -->|Route A| C[Specialized Task A]
    B -->|Route B| D[Specialized Task B]
    B -->|Route C| E[Specialized Task C]

Highlights: Implements contextual awareness via intelligent routing logic. Ideal for multi-domain or customer-facing systems.

4. Orchestrator-Worker Pattern

A scalable architecture where an orchestrator decomposes complex goals into smaller subtasks and distributes them among specialized workers.

graph TD
    START --> A[Orchestrator]
    A --> B[Task Planning]
    B --> C[Worker Pool]
    C --> D[Worker 1]
    C --> E[Worker 2]
    C --> F[Worker N]
    D --> G[Result Synthesis]
    E --> G
    F --> G
    G --> END

Highlights: Promotes modularity and horizontal scaling. Workers handle specific subtasks while the orchestrator manages coordination and result aggregation.

5. Evaluator-Optimizer Pattern

An iterative feedback-driven design that improves quality by alternating between generation and evaluation phases until convergence.

graph TD
    START --> A[Content Generator]
    A --> B[Evaluator]
    B --> C{Quality OK?}
    C -->|Yes| END
    C -->|No| D[Generate Feedback]
    D --> A

Highlights: Combines evaluation and generation in cycles to achieve optimal outcomes. Ideal for quality-sensitive tasks such as code, content, or design generation.

6. Agent Pattern

The most autonomous structure where agents reason, choose tools, and act based on evolving context without predefined control flow.

graph TD
    START --> A[Agent Controller]
    A --> B[Decision Engine]
    B --> C{Action Needed?}
    C -->|Tool Call| D[Tool Execution]
    C -->|Direct Response| END
    D --> E[Result]
    E --> A

Highlights: Designed for dynamic decision-making and adaptive responses. Ideal for reasoning, information retrieval, or self-directed problem-solving systems.

Pattern Selection Matrix

Pattern	Predictability	Complexity	Parallelization	Autonomy
Prompt Chaining	High	Low	No	Low
Parallelization	High	Medium	Yes	Low
Routing	Medium	Medium	No	Low
Orchestrator-Worker	Medium	High	Yes	Medium
Evaluator-Optimizer	Medium	Medium	No	Medium
Agent	Low	High	Conditional	High

Advanced Integrations

Hybrid Patterns

Combine multiple paradigms for adaptive intelligence:

Router + Orchestrator-Worker → domain-specific task routing with distributed execution.
Agent + Evaluator-Optimizer → self-learning, self-correcting intelligent systems.

Human-in-the-Loop

Integrate human oversight for ethical control, compliance, or quality assurance.

graph TD
    A[Automated Flow] --> B{Human Review Needed?}
    B -->|Yes| C[Human Input]
    B -->|No| D[Continue]
    C --> D
    D --> E[Final Output]

Conclusion

Workflow and agent patterns form the building blocks for reliable, scalable, and adaptive AI systems. Select patterns based on task complexity, autonomy, and performance needs, and extend them with hybrid or human-in-loop approaches for production-grade applications.