An AI agent is a software system that receives a goal, reasons about how to achieve it, and takes a sequence of actions using available tools until the goal is complete or it determines the goal cannot be achieved. It is different from a standard AI chatbot in one important way: a chatbot responds to a message and stops. An agent responds by doing something in the world: searching the internet, reading a document, writing and executing code, calling an API, filling in a form, or sending a message. The agent continues acting until the task is done, not just until it has said something about the task.
Every current AI agent uses a large language model as its reasoning engine. The LLM reads the goal, the available tools, and the current state of the task, then decides what to do next. GPT-4, Claude, and Gemini are the most common LLMs used in commercial AI agents in 2026. The quality of the LLM's reasoning directly determines the reliability of the agent.
Tools are functions the agent can call to interact with the world beyond text generation. Common tools include: web search (to find current information), code execution (to run calculations or data transformations), file reading and writing, API calls (to interact with external software systems), and email or calendar access. An agent without tools can only generate text. An agent with the right tools can complete real-world tasks.
Agents have two types of memory. Short-term memory is the current conversation and task context, held within the active session. Long-term memory is information stored and retrieved from a database across sessions, allowing the agent to remember past tasks, preferences, and relevant facts. Long-term memory is what allows an agent to say I see from last month's report that this client has historically paid late, so I will flag this invoice for follow-up in 14 rather than 30 days.
The planning loop is the reasoning process the agent uses to decide what action to take next, given the current state. The most common approach is: think about what is needed, take one action, observe the result, think about what is needed next, and repeat. This loop continues until the agent completes the task or reaches a predetermined limit on the number of steps it is permitted to take.
Task agents execute a specific repeatable task autonomously. A task agent might handle all incoming support emails: reading each email, categorising it, checking the knowledge base for a relevant answer, drafting a response, sending it if confidence is high, or routing to a human if confidence is low. Task agents are the most common type deployed by UK SMEs in 2026 because they are the most reliable: the task is well-defined, the success criteria are clear, and errors are catchable.
Research agents search multiple sources, synthesise information, and produce a structured output. A sales research agent receives a company name and produces a briefing covering the company's size, recent news, key decision-makers, likely pain points, and relevant case studies from the agency's own client base. What previously took a salesperson 90 minutes takes a research agent four minutes.
Orchestrator agents coordinate other agents. A complex workflow involving document extraction, data validation, CRM update, and client notification might be handled by four specialised sub-agents, each responsible for one step. An orchestrator agent manages the sequence, passes outputs between agents, handles exceptions, and reports the final status to a human or another system. This architecture makes complex automation more reliable because each sub-agent is optimised for one task rather than one general agent trying to handle everything.
AI agents fail in specific, predictable ways. Understanding these failure modes is as important as understanding their capabilities.
Long-horizon planning: Agents struggle with tasks requiring more than eight to twelve sequential steps without human checkpoints. The reasoning error rate compounds across steps. A 90-step autonomous process with a 95% accuracy per step has a 1% probability of completing correctly from start to finish. Break long processes into supervised segments.
Ambiguous goals: Agents optimise for what they are told to do. If the goal is ambiguously specified, the agent will complete it in a technically correct but contextually wrong way. A goal of increase sales pipeline given to an agent with email access will generate emails, because that is technically a pipeline activity. The quality and appropriateness of those emails depend entirely on how well the goal and constraints are specified.
Novel situations: Agents trained on patterns in data struggle when situations fall outside those patterns. Human oversight is essential for any agent operating in a domain where genuinely novel situations occur regularly.
Start with one task. Choose a task that: happens more than 30 times per month, has a clear definition of a correct completion, involves interactions with two or three software systems, and currently takes a human 20 to 60 minutes to complete. Build an agent for that one task. Measure its accuracy and reliability over 30 days. The learning from one successful agent deployment will inform every subsequent deployment more than any amount of prior planning.
AI agents are a type of AI automation, but not all AI automation uses agents. Traditional automation follows fixed rules. AI agents reason through variable situations using language models and take actions based on that reasoning. Agents are better suited to tasks with variability and complexity. Fixed-rule automation is better suited to highly structured, predictable tasks where reliability is critical and reasoning is not needed.
Well-designed AI agents include error handling: they detect when a step fails or produces an unexpected result, log the failure, and either attempt a recovery action or escalate to a human. Poorly designed agents continue regardless of errors, compounding mistakes across subsequent steps. Always include human escalation paths in agent workflows, especially during initial deployment.
A multi-agent system uses multiple AI agents working together, each specialised for a specific part of a workflow. One agent handles research, another handles drafting, another handles review, and an orchestrator manages the sequence. Multi-agent systems are more reliable than single agents for complex tasks because each agent is optimised for a narrow function rather than one agent attempting to do everything.
To explore which tasks in your business are suitable for AI agent deployment, see our AI Process Automation service or learn more about our AI Projects.
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