An AI agent is a software system that uses a large language model as its reasoning core but extends beyond simple question-answering to take actions in the world. Where a standard chatbot responds to one message at a time, an agent perceives its environment, breaks a goal into steps, selects and invokes tools (APIs, databases, web browsers, code interpreters, other agents), observes the results, and continues planning until the task is complete — often without a human in the loop for each individual step.
What Makes an Agent Different from a Chatbot
The defining characteristics of an AI agent are tool use, memory, and autonomous planning. Tool use means the agent can call real systems — run a database query, send an email, execute code, or submit a form. Memory allows it to retain context across a long task or session, not just the last message. Autonomous planning means it can decompose a vague goal ("prepare the monthly board report") into ordered subtasks, execute them, handle errors, and deliver a result.
Common Use Cases
- Operations automation:Agents that monitor incoming supplier invoices, extract line items, cross-check purchase orders, flag discrepancies, and route approvals — end to end, without manual processing.
- Research and summarisation:An agent that searches the web, pulls regulatory filings, and writes a structured briefing document on a competitor or market.
- Code and DevOps:Agents that triage bug reports, write fixes, run tests, and open pull requests, or that monitor infrastructure alerts and take remediation steps.
- Customer service escalation:An agent that retrieves the customer's order history, checks courier APIs, identifies the delay cause, and drafts a resolution email — all before a human support agent reads the ticket.
Multi-Agent Systems
Complex workflows often involve multiple specialised agents working in coordination: an orchestrator agent that plans the task hands off sub-tasks to specialist agents (a data analyst agent, a report-writing agent, a QA agent) and assembles the final output. Frameworks like LangGraph, AutoGen, and CrewAI provide infrastructure for designing these multi-agent pipelines with appropriate guardrails.
AI Agents at Dictode
Dictode builds production AI agent systems for businesses that want to automate knowledge work, not just surface information. We design the agent architecture, choose appropriate tools and memory backends, instrument for observability, and build the human-in-the-loop review points required for high-stakes decisions. If you have a manual process that follows a defined logic but consumes significant skilled time, an AI agent is likely the right solution.