The Machine Learning Practitioner’s Guide to Agentic AI Systems

In this article, you’ll learn how practitioners can evolve from traditional machine learning workflows to designing, building, and shipping production-ready AI systems.

Topics we will cover include:

• What makes an AI system “effective” and why does it matter to practitioners?
• The basic architectural styles (ReAct, Plan-and-Execute, and Reflexion) and when to use each.
• Practical frameworks, projects and resources for developing portfolio-ready agent skills.

Let’s not waste any more time.

introduction

Agent artificial intelligence (AI) represents the most significant transformation in machine learning since deep learning transformed the field. Instead of building interactive tools that respond to prompts, practitioners now design autonomous systems that plan, reason, and act autonomously to achieve complex goals. This shift is reshaping how we approach machine learning problems, from simple classification tasks to complex, multi-step workflows that require strategic thinking and tool use.

For machine learning and data science practitioners, this evolution naturally builds on your existing foundation. The core skills I developed—rapid engineering, working with large language models (LLMs), and building augmented retrieval generation (RAG) systems—are now the building blocks for creating agent systems. Transitioning requires learning new architectural patterns and frameworks, but you start from a position of strength.

In this guide, you’ll discover a step-by-step approach to moving from traditional machine learning to agentic AI. You’ll learn key concepts, explore the most effective frameworks, access the best learning resources, and understand how to build production-ready agents that solve real problems. This guide is designed for practitioners who want results, not just theory.

Ground yourself in the basics

Before diving into agent frameworks, you need to understand what makes AI an “agent” and why it is important.

Agent AI refers to autonomous systems that pursue goals autonomously Through planning, reasoning, use of tools and memory, rather than simply responding to prompts. While traditional MBA students are reactive (you ask, they answer), agentic systems proactively break down complex tasks, make decisions, use tools, learn from feedback, and adapt their approach without constant human guidance.

If you’re already working with LLMs, you have exactly the foundation you need. Agentic AI builds directly on Rapid Engineering, RAG systems, and LLM applications. If you need a refresher, check out our guides Rapid engineering, Our RAG seriesand LLM Applications.

Start here (free): AI Agent with Andrew Ng. This is your best first step. It’s free during the trial period and teaches essential design patterns from a leading expert.

Learn basic architectural patterns

The key to building effective clients is understanding how they think and act. There are three basic constructs that every practitioner should know.

Reaction (inference and action) It is the most common starting pattern. The agent alternates between thinking about what to do, taking action with a tool, observing the outcome, and repeating until the task is complete. It is easy to implement and works well for straightforward tasks, but it can be expensive because it requires an LLM call for each step.

Planning and implementation Separates planning from implementation. The agent first creates a complete multi-step plan, then implements each step (often using smaller, cheaper models), and adjusts the plan if necessary. This approach is often faster and cheaper than ReAct for complex workflows, making it a preferred choice for production systems in 2025.

reflection Adds self-improvement through linguistic feedback. The agent explicitly critiques its responses, retains memory of previous attempts, and improves its approach based on failure. They are especially valuable for research-intensive, high-risk applications where correctness is more important than speed.

Understanding these patterns helps you choose the right architecture for your use case. Simple customer service inquiries? ReAct works great. Complex multi-step workflows such as data analysis pipelines? Planning and implementation. Search agents who need accuracy? reflection.

Learn more (free): Take AI Agent Design Patterns with AutoGen Take a course on DeepLearning.AI to see these patterns in action.

Choose your framework and learn it in depth

This is where theory meets practice. You need to choose a framework and build real systems using it. The space has three dominant players in 2025: langgraf, CrewAIand Automatic creation. Each framework serves different needs.

langgraf It is the standard for production systems. It provides granular control with graph-based workflow, built-in state management, and excellent monitoring capability through LangGraph Studio and LangSmith. If you need a complex and efficient workflow with detailed monitoring, this is the framework for you. The learning curve is steeper, but it’s worth it for professional publishing.

CrewAI It is the fastest way to get started using multi-agent systems. Its turn-based design makes it intuitive. You can define agents with specific personalities and responsibilities, assign tasks, and let them collaborate. It’s well suited for content creation, research funnels, and any scenario where you can think in terms of “team roles.”

Automatic creation (Now part of the Microsoft Proxy Framework) Excels at multi-agent conversational patterns. It is ideal for complex collaboration between agents and enterprise Microsoft environments. The March 2025 update introduced a unified SDK, agent-to-agent protocol, and seamless Azure AI Foundry integration.

Choose one frame to start. Don’t try to learn all three at once. For most practitioners, start with CrewAI for rapid prototyping, then learn LangGraph when you need production-grade control.

Build practical projects that demonstrate skills

Theory without practice will not provide you with opportunities. You need portfolio projects that demonstrate your ability to build production-ready agents.

Start simple: Building a research agent Which takes a question, searches multiple sources, gathers information, and provides a quoted answer. This project teaches you tool integration (web search), memory management (trace sources), and response generation.

Next level: Create a multi-agent content creation system. Identify agents with specific roles: researcher, writer, editor, fact-checker. Then coordinate them to produce polished goods. This demonstrates an understanding of agent coordination and task delegation. Our educational program is on Building Your First Multi-Agent System: A Beginner’s Guide Walks through this with CrewAI.

advanced: Build an independent data analysis agent Which connects to your databases, explores data based on natural language queries, generates insights, creates visualizations, and identifies anomalies – all without human step-by-step guidance. This showcases RAG techniques, tool use, and layout capabilities.

Hands-on resources:

Learning memory systems and advanced patterns

What separates novice software developers from experts is an understanding of memory and advanced thinking.

Memory systems They are essential for customers who need context across conversations. Short-term memory (session state) deals with current interactions using tools such as redis Or built-in LangGraph Checkpoint. Long-term memory requires further development: vector stores for semantic retrieval, cognitive graphs of structured facts with temporal tracking, and summarization strategies to prevent memory inflation.

Best practices for 2025 are a Hybrid approach: Vector search for semantic retrieval, cognitive graphs for real-world accuracy and updates, and decay strategies for growth management. LangGraph’s LangMem module and Redis Agent Memory Server are production-proven solutions.

Advanced patterns To learn include agent RAG (where agents decide when to retrieve information and create targeted queries), multi-agent coordination (a “puppeteer” style where a trained coordinator dynamically directs specialized agents), and human-in-the-loop workflow (escalating important decisions while maintaining autonomy for routine tasks).

the Model Context Protocol (MCP)widely adopted in 2025, is transforming agent communication. Learning MCP now future-proofs your skills as it becomes the standard for connecting agents to tools and data sources.

Deep resources:

Put your learning into practice

You now have a comprehensive roadmap from foundations to applications. As you develop these skills, you’ll find opportunities across a range of roles: AI Engineer, Machine Learning Engineer (with an agent focus), AI Engineer, MLOps Engineer, and Emerging Agent Coordinator position. These roles span from entry-level to senior positions in various industries, and all require the foundational knowledge you’ve gained from this guide.

The field of agent AI is growing rapidly, with the market expanding from $5-7 billion in 2025 to $50-200 billion by 2030-2034. Organizations across financial services, healthcare, retail, and professional services are actively deploying agent systems. This growth creates opportunities for practitioners who understand the technical foundations and practical implementation of agent systems. Practitioners who develop these skills now place themselves at the forefront of this rapidly evolving field.