For most of 2023 and 2024, the AI conversation centred on large language models: ChatGPT, Claude, Gemini. These are impressive, but they are reactive. You type a question; they produce an answer. One turn. One output. Then they stop.
Agentic AI is fundamentally different. An AI agent doesn't just answer, it acts. It takes a goal, figures out the steps needed to achieve it, uses tools to execute those steps, evaluates the results, and continues, all without a human typing the next instruction. To understand why everyone needs to learn AI implementation, this shift is central.
This is the shift from AI as a search engine to AI as a colleague.
What is an AI agent?
An AI agent is a software system built around a large language model that can:
- Plan, break a high-level goal into a sequence of sub-tasks
- Use tools, call functions such as web search, code execution, database queries, email, or any API
- Observe, receive the results of each tool call and incorporate them into its reasoning
- Adapt, change its plan based on what it discovers, handling errors and unexpected results
- Complete, deliver a final result when the goal is achieved
The key insight is the loop: Plan → Act → Observe → Plan again. This is what separates an agent from a chatbot.
What is the difference between AI and Agentic AI?
Think of it this way:
- Standard AI (LLM): "Summarise this document." → Summary. Done.
- Agentic AI: "Research the top 5 competitors in our market, analyse their pricing pages, and write a comparison report." → The agent searches the web, visits pages, extracts data, organises it, and delivers a formatted report, without you doing anything in between.
Agentic AI is the difference between a very knowledgeable assistant you have to guide step by step, and one that can take a brief and run with it.
Real-world examples of AI agents
Agentic AI is already in production across many industries:
- Software development, coding agents that read a codebase, identify bugs, write tests, and submit pull requests
- Customer service, agents that look up order history, process refunds, check stock, and escalate only genuinely complex cases
- Research and analysis, agents that search the web, read PDFs, extract data, and produce structured reports
- Data science, agents that load a dataset, run exploratory analysis, select models, evaluate performance, and summarise findings
- Sales and marketing, agents that qualify leads, draft personalised outreach, and schedule follow-ups
Multi-agent systems
The most powerful agentic architectures use multiple agents working in parallel or in sequence. An orchestrator agent breaks a large task into sub-tasks and delegates them to specialist agents, one for research, one for writing, one for code review. This mirrors how teams of humans work, and it enables agents to tackle problems far too complex for any single model call.
What skills do you need to build AI agents?
Building production-grade AI agents requires a specific stack of knowledge: