In a recent LinkedIn post, Andrew Bolis outlines a comprehensive 10-step roadmap for building AI agents from scratch, emphasizing a structured approach over complex coding. Bolis asserts that the sequence of steps is crucial for efficient AI agent development.
“Anyone can build AI agents from scratch. Use this 10-step roadmap from idea to evaluation.”
Defining the Agent’s Core Functionality
The initial phase, as detailed by Bolis, involves clearly defining the agent’s purpose and expected outcomes. This includes specifying what the agent will do, who it will serve, and the nature of its output. Bolis provides an example of a content research agent designed to review articles and summarize key points.
Structuring Inputs and Outputs
Following the purpose definition, Bolis stresses the importance of building structured input and output formats. He recommends using tools like Pydantic AI or JSON Schemas to ensure a clean, API-like data structure. This standardization is key for consistent agent performance.
Shaping Agent Behavior and Reasoning
Andrew Bolis then delves into shaping the agent’s behavior and enabling its reasoning capabilities. He suggests starting with a clear, role-based system prompt and utilizing techniques like Prefix Tuning or Prompt Tuning for consistent actions. For reasoning, Bolis points to frameworks like ReAct or Chain-of-Thought, which allow agents to break down complex problems and access external tools.
“Use ReAct or Chain-of-Thought for reasoning steps. Enable tools like search, code, or document lookup.”
Integrating Multi-Agent Systems and Memory
For more complex applications, Bolis’s roadmap addresses the organization of multi-agent roles. He highlights orchestration frameworks like LangGraph or CrewAI that allow for the definition of specialized agents, such as Planners, Researchers, and Writers. Furthermore, Bolis emphasizes the need for memory and extended context, particularly through Retrieval-Augmented Generation (RAG), to enable agents to recall past interactions or access vast datasets.
“Decide if the agent needs context from earlier work. Use summary, conversational, or vector memory.”
Enhancing Agent Capabilities and Delivery
Bolis’s guide also touches upon optional enhancements, including speech and vision capabilities. He mentions tools like Coqui or ElevenLabs for speech synthesis and vision models for image interpretation, allowing agents to interact more dynamically. The output formatting and delivery are also crucial, with options ranging from Markdown and PDF to JSON, ensuring results are predictable and usable.
Deployment and Continuous Improvement
The final stages of Bolis’s roadmap focus on deployment and ongoing improvement. He suggests embedding agents within a UI using tools like Streamlit or Gradio, or exposing them via an API with FastAPI, effectively transforming them into accessible products. The critical last step involves rigorous testing, review, and iterative improvement, using logs and benchmarks to measure and enhance reliability.
“Run test prompts to measure reliability. Use logs and benchmarks to improve performance.”
Andrew Bolis concludes by reiterating that effective AI agent development hinges on a well-defined structure from the outset, rather than solely on coding prowess. His detailed post serves as a practical guide for developers looking to build and deploy AI agents efficiently.
📝 About This Content
This article is based on insights shared by Andrew Bolis on LinkedIn.
📅 Originally posted on December 3, 2025 | View original post on LinkedIn →