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:
- Python, the foundation (our NCFE Programming qualifications cover this; see also our beginner's guide to Python for data science)
- LLM APIs, calling OpenAI, Anthropic, or open-source models, and understanding context windows, tokens, and costs
- Tool calling / function schemas, defining tools that the LLM can invoke
- Orchestration, managing multi-step workflows, state, and agent memory
- Observability, logging, tracing, and evaluating agent behaviour to ensure reliability
- Safety, preventing prompt injection, data leakage, and runaway agent loops
Why Agentic AI matters for your career
The UK government's AI Opportunities Action Plan (2025) identifies agentic AI as a strategic priority. Enterprises are moving from AI experimentation to AI deployment, and deployment means agents. The demand for developers who can build, evaluate, and maintain agent systems is growing faster than supply.
This is not a technology that will arrive in five years. It is here, in production, at major UK employers right now.
Frequently asked questions
What is Agentic AI?
Agentic AI refers to AI systems that can plan, reason, and take actions autonomously to complete multi-step tasks, using tools like web search, code execution, and APIs without constant human input between each step.
What is the difference between AI and Agentic AI?
Standard AI responds to a single input and produces a single output. Agentic AI takes a high-level goal, breaks it into steps, uses tools to execute those steps, and adapts its approach based on intermediate results, operating in a continuous Plan → Act → Observe loop.
What are examples of AI agents?
Real-world examples include coding agents that fix bugs autonomously, research agents that search the web and produce reports, customer service agents that process refunds without human involvement, and data analysis agents that load and evaluate datasets end-to-end.
What skills do I need to build AI agents?
You need solid Python, experience with LLM APIs, understanding of tool calling, knowledge of orchestration frameworks, and skills in observability and safety evaluation.