AI Agents

AI agents are systems built on top of large language models that can autonomously plan and execute multi-step tasks. Unlike a standard chatbot that responds to individual prompts, an agent can break down a complex goal into sub-tasks, decide which tools to use, execute actions, observe results, and adapt its approach based on what it learns along the way. The key distinction is autonomy. A chatbot answers questions; an agent accomplishes objectives. An agent might research a topic across multiple sources, write a report, send it for review, incorporate feedback, and publish the final version — all from a single high-level instruction.

Most AI agents follow a loop of reasoning, acting, and observing. The agent receives a goal, uses an LLM to reason about what steps are needed, selects and executes an action (such as calling an API, querying a database, or writing code), observes the result, and then decides the next step. This cycle continues until the goal is achieved or the agent determines it cannot proceed. Agents are typically equipped with tools — interfaces to external systems like web browsers, code interpreters, databases, email services, or internal APIs. The model's ability to choose the right tool for each step, interpret the results correctly, and handle errors gracefully is what separates a capable agent from a fragile one. Modern agent frameworks also incorporate memory (both short-term context and long-term storage), planning capabilities, and reflection mechanisms that allow agents to evaluate and improve their own performance.

AI agents represent a significant leap from AI as a tool to AI as a worker. They can handle end-to-end workflows that previously required human coordination across multiple systems — processing invoices, onboarding customers, conducting research, managing IT tickets, and orchestrating data pipelines. The business value is substantial: agents can work around the clock, handle high volumes consistently, and free skilled employees from repetitive multi-step processes. However, deploying agents requires careful consideration of trust boundaries, error handling, and human oversight. The most successful deployments start with well-defined, lower-risk workflows and gradually expand the agent's autonomy as confidence builds.

AI agents are being deployed across industries for tasks including automated customer support escalation (where agents gather information, attempt resolution, and route complex cases to humans), research and analysis workflows (where agents search, synthesise, and report on topics), and software development (where agents write, test, and debug code). In operations, agents automate data entry, report generation, and cross-system synchronisation. In sales, they qualify leads, draft proposals, and update CRM records. The common thread is multi-step processes that follow logical patterns but previously required human judgement at each step.