Agent Workflow Implementation Patterns

This article is a continuation of the previous article, where we covered the Agentic Architecture and model. In this article, we will discuss the workflow implementation pattern

1. Reflection
2. Tool Use
3. Planning
4. Multi-Agent Collaboration

1. Reflection

Reflection is an essential design pattern in which AI systems analyze their own outputs to enhance performance. A dedicated agent focuses on error detection and refinement, improving result quality. This iterative approach enables AI to learn from past mistakes and continually optimize its processes. By promoting continuous improvement, organizations can achieve greater accuracy and reliability in AI-generated outcomes.

This diagram illustrates the Reflection Pattern, commonly used in AI agent architectures. Here's a breakdown:

1. User Input: The process begins with input from the user.
2. Actor Agent: This agent processes the input and generates a response.
3. Review Agent: The response is then passed to the review agent, which evaluates or refines it.
4. Review Cycle: The review agent can send feedback to the actor agent for improvements.
5. Final Response: Once reviewed, the response is either finalized or iterated upon.

This pattern helps improve responses through iterative refinement, making it useful in AI-driven content generation, decision-making, and self-improving systems.

Number of iteration need to be controlled

lets consider an example :

Understanding the Diagram

The diagram illustrates a reflection pattern in AI agents using a book review process. It consists of two main cycles:

• Actor Cycle (Primary Processing)
• Review Cycle (Reflection and Refinement)

Components and Flow

• Book Review Input
• The process starts when a book review is submitted as input.

2. Actor Agent (Actor Cycle)

• This agent is responsible for analyzing the book review.
• It reviews the content and generates an initial response.
• This response is then passed to the Review Agent for further validation.

3. Review Agent (Review Cycle)

• The Review Agent evaluates the response generated by the Actor Agent.
• It checks for accuracy, coherence, and completeness.
• If needed, it refines the response and generates an improved version.

4 Reflection and Iteration

• The Review Agent can send feedback to the Actor Agent.
• This process continues iteratively until an optimal response is achieved.
• The final response is then provided as output.

2. Tool

Agentic Workflows leverage external tools and APIs, such as search engines, calculators, and real-time data extraction, to enhance the capabilities of Large Language Models (LLMs). By connecting LLMs to diverse external resources, this approach extends their functionality beyond text processing, enabling more dynamic and versatile applications. This integration allows AI to engage in richer interactions and tackle complex problem-solving tasks more effectively

This diagram represents the Tool Pattern in an Agentic Workflow, which involves integrating external tools to extend the capabilities of an AI agent. Here's a breakdown:

1. User Input: The process begins when a user provides input.
2. Agent: The agent processes the input and determines if external information is needed.
3. Tool: The agent invokes a tool (such as a search engine, calculator, or API) to retrieve or process the required data.
4. External Sources: The tool interacts with multiple external sources to gather information. These could be databases, APIs, web services, or other external resources.
5. Response: The gathered data is returned to the agent, which processes it and provides a final response to the user.

Key Benefits of the Tool Pattern:

• Expands the agent's knowledge and capabilities beyond its training data.
• Enables real-time data retrieval and enhanced problem-solving.
• Improves accuracy and relevance of responses by leveraging external sources.

3. Planning

Planning is essential for determining the tools and models required for specific tasks. This part trains AI to decompose complex tasks into smaller, actionable steps. Effective planning ensures efficient resource allocation and task execution, reducing the risk of oversight.

Planning Pattern Component

1. Planning

• In this initial stage, the AI agent interprets the prompt and devises an overall plan.
• The plan outlines how the AI intends to tackle the problem, including high-level goals and strategies.

2. Generate Task

• From the plan, the AI system generates specific tasks that must be executed.
• Each task represents a smaller, manageable portion of the overarching goal, allowing the AI to work in focused steps.

3. Single Task Agent

• The Single Task Agent is responsible for completing each task generated in the previous step.
• This agent executes each task using predefined methods like ReAct (Reason + Act) or ReWOo (Reasoning WithOut Observation).
• Once a task is completed, the agent returns a Task Result, which is sent back to the planning loop.

4. Replan

The Replan stage evaluates the Task Result to determine if any adjustments are needed.

If the task execution does not fully meet the desired outcome, the system will replan and possibly modify the tasks or strategies.

This feedback loop allows the AI system to learn and improve its approach iteratively, making it more adaptable to changing requirements or unexpected outcomes.

5. Iterate:

This part of the pattern is a loop connecting Generate Task and Replan.

It signifies the iterative nature of the process, where the AI system continuously re-evaluates and adjusts its approach until it achieves satisfactory results.

3. Planning

Planning is essential for determining the tools and models required for specific tasks. This part trains AI to decompose complex tasks into smaller, actionable steps. Effective planning ensures efficient resource allocation and task execution, reducing the risk of oversight.

Planning Pattern Component

1. Planning

• In this initial stage, the AI agent interprets the prompt and devises an overall plan.
• The plan outlines how the AI intends to tackle the problem, including high-level goals and strategies.

2. Generate Task

• From the plan, the AI system generates specific tasks that must be executed.
• Each task represents a smaller, manageable portion of the overarching goal, allowing the AI to work in focused steps.

3. Single Task Agent

• The Single Task Agent is responsible for completing each task generated in the previous step.
• This agent executes each task using predefined methods like ReAct (Reason + Act) or ReWOo (Reasoning Without Observation).
• Once a task is completed, the agent returns a Task Result, which is sent back to the planning loop.

4. Replan

The Replan stage evaluates the Task Result to determine if any adjustments are needed.

If the task execution does not fully meet the desired outcome, the system will replan and possibly modify the tasks or strategies.

This feedback loop allows the AI system to learn and improve its approach iteratively, making it more adaptable to changing requirements or unexpected outcomes.

5. Iterate:

This part of the pattern is a loop connecting Generate Task and Replan.

It signifies the iterative nature of the process, where the AI system continuously re-evaluates and adjusts its approach until it achieves satisfactory results.

Example :

How This Pattern Works in AI Systems

• Autonomous AI Workflows

AI planning agents are used in self-improving systems where evaluation refines the output.

• Computer Vision Applications

Used in image recognition, surveillance, and automated tagging in digital media.

• AI-Powered Search and Metadata Generation

AI systems can identify and categorize content automatically, improving search capabilities.

Key Benefits of AI Planning Agents

? Task Decomposition – Splitting tasks improves efficiency.

? Evaluation and Feedback Loop – Ensures better accuracy.

? Adaptability – Can refine results iteratively.

4. Multi-Agent Collaboration

Multi-agent collaboration involves several agents operating concurrently in distinct roles without interference. Each agent contributes its unique expertise, ensuring tasks are managed effectively. This cooperative approach maximizes efficiency by utilizing diverse skill sets and promoting seamless coordination. As a result, it speeds up task execution and improves overall output quality.

Above diagram shows

• User Input: The process begins with input from the user.
• Agent 1: The first agent processes the input and passes the data to the next agent in the workflow.
• Agent 2 & Agent 3: These agents continue processing based on their specialized roles.
• Agent 3 → Agent 4: After processing, Agent 3 transfers the task to Agent 4 for further refinement or execution.
• Agent 4 → Agent 5: The output is further refined or validated before reaching the final stage.
• Collaboration: Agents can interact dynamically, forming a network rather than a strict sequence, enabling flexible problem-solving.

Multi-Agent Example – Travel Planning System

This diagram illustrates a multi-agent system designed for travel planning, where multiple agents collaborate to process a user's request efficiently.

User Input:

• The process begins when a user submits a request for travel planning (e.g., booking flights, hotels, and rental cars).

Travel Planner Agent:

• Acts as the central coordinator that manages and delegates tasks to specialized agents.
• It interacts with an LLM (Large Language Model) Integration to enhance decision-making or refine user queries.

Specialized Agents (Flight, Hotel, and Car Rental Agents):

• These agents handle specific travel-related tasks:Flight Agent: Searches for available flights.Hotel Agent: Finds accommodation options.Car Rental Agent: Looks for rental car services.

Perform Web Search:

• The specialized agents collectively perform searches on external sources (e.g., travel websites, APIs).
• The retrieved information is then consolidated into a single response

Final Output:

• The system provides the user with a comprehensive travel plan, including flights, hotels, and car rentals in a structured format.

Key Benefits of This Multi-Agent System:

? Parallel Execution: Each agent works independently, making the process faster.

? Specialization: Different agents focus on specific tasks, ensuring efficiency.

? Enhanced Decision-Making: LLM integration improves response quality.

? Scalability: Additional agents (e.g., restaurant recommendations, sightseeing suggestions) can be integrated.