How To Build An Agentic Ai 2025 Step By Step Tutorial Code

Bonisiwe Shabane

-Dec 28, 2025, 3:58 PM

how to build an agentic ai 2025 step by step tutorial code

This tutorial walks you from a runnable single‑agent to production‑ready practices and a small multi‑agent demo. You’ll build with LangGraph first (Python), then add memory, observability, safety, and evaluation. Finally, you’ll see alternatives (CrewAI multi‑agent, LlamaIndex RAG, and a lightweight AutoGen sample). Why this approach in 2025: Agentic systems benefit from graph‑based orchestration (clear states, tools, and evaluators), which is the core design in LangGraph, as outlined in the LangGraph introduction by LangChain (2024/2025). Create and activate a virtual environment, then install the essentials. Add your OpenAI key to a .env file in the project root:

We’ll create an agent that can call a calculator and a web search tool. Tool use is first‑class in the LangChain ecosystem, with models emitting function/tool calls that your graph executes, as covered in the LangChain tool calling docs (2024/2025). You may have heard about “Agentic AI” systems and wondered what they’re all about. Well, in basic terms, the idea behind Agentic AI is that it can see its surroundings, set and pursue goals, plan and reason through many processes, and learn from experience. Unlike chatbots or rule-based software, agentic AI actively responds to user requests. It may break activities into smaller tasks, make decisions based on a high-level goal, and change its behavior over time using tools or other specialized AI components.

To summarize, agentic AI systems "solve complex, multi-step problems autonomously by using sophisticated reasoning and iterative planning." In customer service, for example, an agentic AI may answer questions, check a user's account, offer balance... So, agentic AI is "AI with agency”. Given a problem context, it sets goals, creates strategies, manipulates the environment or software tools, and learns from the results. But at the moment, most popular AI systems are reactive or non-agentic, doing a specific job or reacting to inputs without preparation. For example, Siri or a traditional image classifier use predefined models or rules to map inputs to outputs. Instead of long-term goals or multi-step processes, reactive AI "responds to specific inputs with pre-defined actions".

Agentic AI is more like a robot or personal assistant that can handle reasoning chains, adapt, and "think" before acting. Artificial Intelligence (AI) is everywhere and transforming the way we live and work – from how we shop online to how doctors diagnose patients, how businesses manage operations, and even how entertainment is created. In 2025, AI is no longer just a chatbot that answers questions. It has evolved into the powerful autonomous systems that can make decisions, solve problems, and do tasks on their own. These smart and advanced autonomous systems are called Agentic AI – intelligent agents that can think, plan, learn from experience, and perform and manage actions without human intervention. Imagine having a digital assistant that doesn’t answer your questions but can gather information, create reports, research market trends, schedule meetings, send emails, automate follow-ups, and even suggest business approaches and strategies.

That’s exactly what Agentic AI can do. 👉 You may also like:Top Web Development Trends in 2025 — to see how AI is transforming modern web technologies. In this beginner-friendly post, we’ll take you step by step through: By following this guide, you’ll not only understand what Agentic AI is but also gain practical knowledge to build a working AI Agent that can perform tasks automatically. These are the simple and easy-to-follow steps to help you get started in the Intelligent and Autonomous world of AI. The advancement of artificial intelligence continues to bring forth new tools and systems that streamline complex processes.

Among the most significant of these are AI agents, which are rapidly becoming essential for businesses seeking to automate tasks and enhance efficiency. A 2024 report by LangChain, a prominent agent framework, revealed that 51% of surveyed professionals are already using AI agents in production, with 78% having active plans for implementation. This guide provides a comprehensive walkthrough of creating AI agents and AI agent development, from foundational concepts to practical implementation, informed by our experience building agents for enterprise clients. An AI agent is a software program that uses artificial intelligence to autonomously perform tasks on behalf of a user or another system. These systems are designed to perceive their environment, make decisions, and take actions to achieve specific goals without constant human intervention. The development of these agents, a process known as creating AI agents, allows organizations to tackle complex objectives affordably, quickly, and at a large scale.

The market for AI agents is projected to reach $56 billion in 2030, a significant increase from $5.4 billion in 2024, highlighting their growing economic importance. While often compared to chatbots or standard AI models, AI agents possess a higher degree of autonomy and complexity. Unlike bots that follow predefined scripts, an AI agent can reason, plan, and adapt its actions based on new information. An AI assistant, for example, typically requires user input and supervision for decision-making, whereas an AI agent can operate independently to accomplish its objectives. This is a key distinction noted by Victor Dibia, a contributor to Microsoft’s AutoGen framework, who observes that enterprises are adopting agents to move beyond simple automation to handle more complex, knowledge-based work. The functionality of an AI agent is built upon several core components that work in concert:

AI agents can be categorized based on their level of intelligence and capability: Just know basic Python? That’s all you need. This roadmap will take you the rest of the way. Agentic AI refers to AI systems that can think, plan, and act independently — not just answer prompts, but use tools, remember context, and complete multi-step tasks like digital employees. But here’s the big question: “How do I start if I don’t have a background in Machine Learning?”

Enter the Agentic AI Roadmap 2025 — a completely free learning path by Dhaval Patel (aka @DhavalSays), designed to help anyone go from beginner to advanced AI builder. Curious about LangChain, LLMs, or AI agents Artificial intelligence is no longer about machines following fixed instructions. In 2025, the fundamental shift is toward agentic AI autonomous systems that can plan, reason, and act independently. Unlike traditional AI models that only respond to prompts, agentic AI agents make decisions, adapt to real-world changes, and collaborate with humans to solve problems. If you’re asking how to build an agentic AI, you’re not alone.

Gartner’s 2025 research shows that over 45% of enterprises are experimenting with agentic AI frameworks, and nearly every industry expects productivity boosts of 20–30%. However, building agentic AI requires more than just coding; it necessitates a roadmap that combines data, infrastructure, and people. This guide breaks down what agentic AI is, why it matters, and how you can build it step-by-step. Traditional AI systems are like calculators; they process inputs and give outputs. Agentic AI systems behave more like teammates. They analyze data, make decisions, and even take actions without waiting for explicit commands.

Dr. Elena Foster, AI Strategy Analyst at Deloitte, explains: Agentic AI is a branch of artificial intelligence focused on building autonomous, intelligent agents capable of making decisions, interacting with other agents and completing complex tasks with minimal human intervention. It combines Generative AI, LLMs, multi-agent systems, memory and workflow orchestration to build advanced AI applications. This Agentic AI tutorial offers a comprehensive guide to core concepts, frameworks, tools and practical projects to master agentic systems. Strong programming skills are important for developing autonomous agents.

This section introduces key programming tools, libraries and frameworks required for developing agentic AI application. Generative AI empowers agents to produce text, code and actions autonomously. Understanding LLMs is critical for applying GenAI inside agentic systems. Prompt engineering is the practice of crafting inputs to get better outputs from LLMs. AI Receptionists, SDRs, Dispatchers, and 99+ roles. Fully trained.

Fully managed. Zero sick days. Agentic AI isn’t just the next step in automation—it’s a paradigm shift. Unlike traditional AI tools that follow static rules, agentic systems perceive, decide, act, and learn autonomously. They don’t just respond—they own tasks from start to finish. This evolution is no longer theoretical.

Enterprises are deploying multi-agent ecosystems to handle complex workflows in real time. The market agrees: the AI agents market is projected to grow at a CAGR of 45.8% through 2030, reaching billions in value (Grand View Research via DataCamp). Forty-five percent of Fortune 500 companies are already piloting agentic AI in finance, healthcare, and legal operations (Market.us). Google and Klarna use multi-agent systems for customer engagement and compliance—proving these platforms are battle-tested in production. Take Klarna’s AI customer service agents: they resolve 78% of inquiries without human intervention, cutting response time from minutes to seconds. This isn’t chatbot scripting—it’s autonomous problem-solving with memory, context, and verification loops.

How To Build An Agentic Ai 2025 Step By Step Tutorial Code

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