In the rapidly evolving landscape of artificial intelligence, a new architectural paradigm is gaining prominence: the sequential AI agent. This approach, which involves chaining together specialized AI agents in a predefined order, offers a powerful way to build predictable, scalable, and maintainable AI systems. By breaking down complex tasks into a series of manageable steps, each handled by a dedicated agent, developers can create robust workflows that deliver high-quality results with greater reliability.

This blog post provides a comprehensive guide to building sequential AI agents. We will explore the core concepts behind this architectural pattern, discuss various implementation strategies using popular frameworks, and share best practices for designing, testing, and deploying these systems in production. Whether you are a seasoned AI developer or just starting your journey, this guide will equip you with the knowledge and tools you need to harness the power of sequential AI agents for your own projects.

The Core of Sequential Agents: Predictability and Specialization

At its heart, the sequential AI agent pattern is about creating a predictable and structured workflow. It draws inspiration from the classic “pipes and filters” architecture in software engineering, where data flows through a series of components, each performing a specific transformation. In the context of AI, these components are specialized agents, each with a clearly defined role and a specific set of capabilities.

This approach stands in contrast to monolithic, single-agent systems, which can become unwieldy and difficult to manage as task complexity increases. By breaking down a large problem into smaller, more manageable sub-tasks, sequential agents offer several key advantages:

Advantage	Description
Modularity	Each agent is a self-contained unit with a specific responsibility, making it easier to develop, test, and maintain.
Specialization	Agents can be optimized for specific tasks, leading to higher-quality outputs and more efficient use of resources.
Predictability	The workflow is deterministic, with a clear and explicit sequence of steps, which simplifies debugging and troubleshooting.
Scalability	New agents can be easily added to the pipeline to extend its functionality, without requiring a complete redesign of the system.

This modular and specialized approach not only improves the reliability of AI systems but also makes them more transparent and easier to understand. By observing the output of each agent in the sequence, developers can gain valuable insights into the system’s decision-making process and identify areas for improvement.

Architecture and Implementation: A Tale of Three Frameworks

While the concept of sequential agents is straightforward, the implementation can vary significantly depending on the chosen framework. To illustrate this, we will explore how to build a simple code generation pipeline using three popular frameworks: LangGraph, Google’s Agent Development Kit (ADK), and CrewAI. Our pipeline will consist of three agents:

1. Code Writer Agent: Generates the initial Python code based on a user’s request.
2. Code Reviewer Agent: Reviews the generated code for errors and suggests improvements.
3. Code Refactorer Agent: Refactors the code based on the reviewer’s feedback.

LangGraph: The Composable Approach

LangGraph, a library from LangChain, provides a flexible and composable way to build agentic workflows. It uses a state-based approach, where each agent is a node in a graph that modifies a shared state object. This makes it easy to create complex, multi-agent systems with clear data flow.

Here is a simplified example of how to implement our code pipeline using LangGraph:

from typing import TypedDict, List
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, START, END

# Define a more detailed state to pass between agents
class CodeGenerationState(TypedDict):
    user_request: str
    generated_code: str
    review_comments: str
    refactored_code: str

# Initialize the language model
llm = ChatOpenAI(model="gemini-2.5-flash")

# Code Writer Agent: Generates the initial code.
def run_code_writer(state: CodeGenerationState) -> CodeGenerationState:
    prompt = f"You are a senior Python developer. Based on the user's request, write the initial Python code. Request: {state["user_request"]}"
    response = llm.invoke(prompt)
    return {"generated_code": response.content}

# Code Reviewer Agent: Reviews the code from the writer.
def run_code_reviewer(state: CodeGenerationState) -> CodeGenerationState:
    prompt = f"You are a code reviewer. Review the following Python code and provide constructive feedback on errors, style, and improvements. Code: {state["generated_code"]}"
    response = llm.invoke(prompt)
    return {"review_comments": response.content}

# Code Refactorer Agent: Refactors the code based on the review.
def run_code_refactorer(state: CodeGenerationState) -> CodeGenerationState:
    prompt = f"You are a code refactorer. Take the original code and the review comments, then refactor the code to address the feedback. Original Code: {state["generated_code"]}nReview Comments: {state["review_comments"]}"
    response = llm.invoke(prompt)
    return {"refactored_code": response.content}

# Build the graph
graph = StateGraph(CodeGenerationState)
graph.add_node("writer", run_code_writer)
graph.add_node("reviewer", run_code_reviewer)
graph.add_node("refactorer", run_code_refactorer)

# Connect the nodes in a sequence
graph.add_edge(START, "writer")
graph.add_edge("writer", "reviewer")
graph.add_edge("reviewer", "refactorer")
graph.add_edge("refactorer", END)

# Compile the graph into a runnable application
app = graph.compile()

In this example, each agent is a node in the StateGraph, and the edges define the sequential flow of the workflow. The AgentState object is passed from one agent to the next, allowing them to share information and build upon each other’s work.

Google’s Agent Development Kit (ADK): The Programmatic Approach

Google’s ADK offers a more programmatic and structured way to build sequential agents. It provides a SequentialAgent class that orchestrates a list of sub-agents in a predefined order. This approach is well-suited for production environments where reliability and explicit control are paramount.

Here is how our code pipeline can be implemented using ADK:

from google.adk.agents.sequential_agent import SequentialAgent
from google.adk.agents.llm_agent import LlmAgent

# Code Writer Agent: Writes the initial Python code.
code_writer_agent = LlmAgent(
    name="CodeWriterAgent",
    model="gemini-2.5-flash",
    instruction="""You are a senior Python developer.
    Based on the user's request, write the initial Python code.
    Output *only* the raw code block.""",
    output_key="generated_code"
)

# Code Reviewer Agent: Reviews the code and provides feedback.
code_reviewer_agent = LlmAgent(
    name="CodeReviewerAgent",
    model="gemini-2.5-flash",
    instruction="""You are a code reviewer.
    Review the Python code provided in the session state under the key 'generated_code'.
    Provide constructive feedback on potential errors, style issues, or improvements.""",
    output_key="review_comments"
)

# Code Refactorer Agent: Refactors the code based on review comments.
code_refactorer_agent = LlmAgent(
    name="CodeRefactorerAgent",
    model="gemini-2.5-flash",
    instruction="""You are a code refactorer.
    Take the original Python code from 'generated_code' and the review comments from 'review_comments'.
    Refactor the original code to address the feedback and improve its quality.""",
    output_key="refactored_code"
)

# Create the sequential agent that orchestrates the pipeline
code_pipeline_agent = SequentialAgent(
    name="CodePipelineAgent",
    sub_agents=[code_writer_agent, code_reviewer_agent, code_refactorer_agent]
)

The SequentialAgent in ADK handles the orchestration automatically, executing each sub-agent in the order they are provided in the list. It also manages the state, passing the InvocationContext from one agent to the next, which allows them to share data through a shared state mechanism.

CrewAI: The Declarative Approach

CrewAI provides a high-level, declarative API for building multi-agent systems. It focuses on making the development process more intuitive and human-readable, with a clear separation of concerns between agents, tasks, and processes. [4]

Here is the CrewAI implementation of our code pipeline:

from crewai import Crew, Process, Agent, Task

# Code Writer Agent: A senior developer who writes the initial code.
code_writer = Agent(
  role='Senior Python Developer',
  goal='Write clean, efficient, and functional Python code based on user specifications.',
  backstory='You are a seasoned Python developer with over 10 years of experience, specializing in writing robust and scalable code.',
  llm='gemini-2.5-flash'
)

# Code Reviewer Agent: A detail-oriented reviewer who checks for quality.
code_reviewer = Agent(
  role='Code Reviewer',
  goal='Thoroughly review Python code for bugs, style issues, and potential improvements.',
  backstory='You are a meticulous code reviewer with a keen eye for detail, ensuring all code meets the highest standards of quality and follows PEP 8 conventions.',
  llm='gemini-2.5-flash'
)

# Code Refactorer Agent: An optimization expert who improves the code.
code_refactorer = Agent(
  role='Code Refactorer',
  goal='Refactor Python code to implement reviewer suggestions and optimize for performance and readability.',
  backstory='You are an optimization expert who excels at transforming code to be more efficient, maintainable, and elegant.',
  llm='gemini-2.5-flash'
)

# Task for the Code Writer Agent
writing_task = Task(
  description='Write a Python function that implements: {user_request}.',
  agent=code_writer,
  expected_output='A single Python code block containing the complete function.'
)

# Task for the Code Reviewer Agent, which depends on the writer's output
review_task = Task(
  description='Review the code for bugs, style issues, and areas for improvement.',
  agent=code_reviewer,
  expected_output='A list of constructive comments and suggestions for the code.',
  context=[writing_task]
)

# Task for the Code Refactorer Agent, which depends on the previous tasks
refactor_task = Task(
  description='Refactor the code based on the provided review feedback.',
  agent=code_refactorer,
  expected_output='The final, refactored Python code block that incorporates all feedback.',
  context=[writing_task, review_task]
)

# Form the crew with a sequential process
code_crew = Crew(
  agents=[code_writer, code_reviewer, code_refactorer],
  tasks=[writing_task, review_task, refactor_task],
  process=Process.sequential
)

# Execute the crew with a sample request
result = code_crew.kickoff(inputs={"user_request": "Create a function to calculate the nth Fibonacci number recursively"})

With CrewAI, you define the agents and their corresponding tasks separately, and then assemble them into a Crew. By setting the process to Process.sequential, you instruct the crew to execute the tasks in the order they are provided. CrewAI handles the state management implicitly, allowing tasks to access the outputs of previous tasks in a contextual manner.

Best Practices and Design Patterns

Building robust and effective sequential agent systems requires more than just choosing the right framework. It also involves adhering to a set of best practices and understanding the broader landscape of agentic design patterns. This section provides practical guidance for designing, building, and deploying sequential agents in production.

Core Principles for Success

Based on insights from industry leaders like Anthropic and Google, several core principles have emerged for building successful agentic systems:

• Start Simple: Begin with the simplest possible solution and only add complexity when necessary. Often, a well-designed prompt or a simple workflow is more effective than a complex multi-agent system.
• Embrace Composability: Build your system from small, reusable components that can be easily combined and reconfigured. This modular approach makes your system more flexible and easier to maintain.
• Prioritize Predictability: For many production use cases, predictability and reliability are more important than full autonomy. Sequential workflows, with their deterministic nature, are often the preferred choice for this reason.

Key Design Patterns for Sequential Agents

While the sequential pattern is powerful on its own, it can be combined with other design patterns to create even more sophisticated and robust systems. Here are some of the most relevant patterns to consider:

Pattern	Description
ReAct (Reason and Act)	Agents alternate between reasoning about a problem and taking action to solve it. This iterative approach allows agents to adapt to new information and changing conditions.
Reflection	Agents critique their own work, identifying errors and areas for improvement. This self-evaluation process can significantly improve the quality of the final output.
Planning	Agents create a detailed plan before they begin executing a task. This upfront planning helps to ensure that the agent has a clear path to success and can avoid potential pitfalls.
Tool Use	Agents are given access to external tools, such as APIs, databases, and code interpreters. This allows them to interact with the outside world and perform a wider range of tasks.
Human-in-the-Loop	At critical junctures in the workflow, the agent pauses and requests input from a human. This ensures that a human is always in control of high-stakes decisions.

By thoughtfully combining these patterns, you can create sequential agent systems that are not only predictable and scalable but also adaptive, robust, and safe.

Practical Recommendations for Implementation

Here are some practical recommendations to keep in mind as you build your sequential agent systems:

• Define Clear Agent Specialization: Each agent in your pipeline should have a clear and focused role. This makes it easier to design, test, and debug your system.
• Establish Explicit Data Flow: The data dependencies between your agents should be explicit and well-defined. This will help to prevent unexpected errors and make your system easier to understand.
• Implement Robust Error Handling: Failures are inevitable in any complex system. Be sure to include mechanisms to handle failures at each step of your pipeline, such as retries, fallbacks, and notifications.
• Test Each Agent Individually: Before you assemble your pipeline, be sure to test each agent individually to ensure that it is working as expected.
• Monitor Your System in Production: Once your system is deployed, be sure to monitor its performance to identify any potential issues and ensure that it is meeting your expectations.

Conclusion: The Future is Sequential

Sequential AI agents represent a significant step forward in the development of reliable and scalable AI systems. By embracing a modular, predictable, and specialized approach, developers can build robust workflows that are easier to design, test, and maintain. While the allure of fully autonomous, generalist agents is strong, the practical reality is that for many real-world applications, the structured and deterministic nature of sequential agents offers a more pragmatic and effective path to success.

As the field of AI continues to evolve, we can expect to see even more sophisticated tools and frameworks for building sequential and other multi-agent systems. However, the core principles of modularity, specialization, and predictability will remain as important as ever. By mastering these principles and thoughtfully applying the design patterns and best practices discussed in this guide, you will be well-equipped to build the next generation of intelligent and reliable AI applications.