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AI agents are getting easier to build, but the surrounding ecosystem of models, SDKs, and frameworks is changing quickly. A lot of agent apps get tricky to maintain since they depend too much on a certain provider, library, or deployment setup. This book looks at a practical alternative, which is to make AI agents whose main logic doesn't change while models, SDKs, and runtimes can be changed around it. It's not about using complicated frameworks. Rather, this book shows you simple architectural patterns that let you set up an agent application so that tools, schemas, prompts, and business logic can stay separate from the runtime layer.

For starters, it'll be a simple loop with agents, and we'll gradually build on that with tools that make things deterministic, outputs in a structured JSON format, and schema validation. It'll teach skills, like switching between models through configuration, running the same agent with hosted models or local inference using vLLM, and isolating SDK-specific integrations behind small adapter layers. Later, we will focus on packaging and deployment, in which we will convert the agent into a command-line tool, expose it through a minimal HTTP API, and package the application using Docker. Ultimately, the book puts the project together as a reusable starter template that can be used as a basis for future agent-based applications.

Instead of talking about shortcuts or automation, this book focuses on practical development patterns for building maintainable AI agents. Basically, this book is perfect for Python developers, software engineers, and AI practitioners who want a step-by-step process for designing agents that can adapt as the surrounding ecosystem changes.

Key Learnings

Build GenAI agents using simple agent loop that accepts prompts, calls tools, and returns structured AI responses.
Use structured JSON outputs and Pydantic schemas to make AI agent responses reliable and safe for automation.
Design AI tools as deterministic Python functions so agents can call calculators, summarizers, and utilities predictably.
Create portable AI agents by separating business logic from LLM and model APIs.
Implement a model gateway pattern to switch between OpenAI models, local LLMs, or other providers via configuration.
Run the same agent with OpenAI models or local LLM inference using vLLM.
Prevent SDK lock-in by isolating AI SDK integrations behind runtime adapters.
Use LLM regression prompts and schema validation for better stability of AI Agents during switching the models.
Package AI agent as CLI tool and HTTP API for real applications and integrations.
Deploy AI agents with Docker containers and environment variable.

Table of Content

Shipping GenAI Agent in Minutes
Building Agent Workflows
Reliable and Structured Agent Output
Switching Models without Rewriting Agent
Running vLLM
Designing Stable Business Logic across Multiple SDKs
Agent Packaging and Deployment

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