What is an AI Agent?
An AI agent is a computer system designed to achieve defined goals through autonomous decision-making, use of external tools, and adaptation to a changing environment. Unlike classical automation scripts (e.g., RPA), an agent does not execute a pre-defined sequence of instructions — it analyses the situation and determines its own path to the objective.
In computer science broadly, the concept of an "agent" has existed since the 1990s, describing any program capable of perceiving its environment and taking action. Today, in the context of foundation models, the term carries new weight. An AI agent is a layer built on top of a large language model (LLM), adding planning, memory, and the ability to call external services.
The key characteristic of an agent is proactivity — it can initiate tasks on its own, monitor processes, and revise plans in response to new information, without requiring the user to issue commands at every step.
Who is behind it?
There is no single creator. Agent architecture grows out of decades of research into intelligent agents in AI (including work on BDI systems — Belief-Desire-Intention). The modern wave of adoption began after 2022, when language models reached sufficient reasoning quality to serve as the "brain" of an agent.
Today the ecosystem is built by dozens of organisations: Microsoft (Copilot Studio, Azure AI Agent Service), Salesforce (Agentforce), Google (Vertex AI Agents), Amazon (Bedrock Agents), and on the open-source side — LangChain and LangGraph as the most widely used frameworks. Enterprise platforms such as SAP, Oracle, and ServiceNow are integrating agents into business software.
How does it work?
An agent operates on an iterative cycle of four steps:
- Perception — the agent takes in inputs: a user query, results of previous actions, the state of external systems.
- Planning — using the LLM (e.g., GPT-4, Claude, Gemini), it breaks the goal into subtasks and defines a sequence of steps. It can construct new workflows dynamically rather than following pre-defined rules.
- Action — it calls tools: APIs, databases, web searches, office applications, business systems. Standards such as Model Context Protocol (MCP) facilitate integration with external platforms like Google Drive or Zapier.
- Evaluation and adaptation — it verifies results, and if it encounters an obstacle (e.g., API unavailability, unexpected output), it modifies the plan or requests human intervention.
The cycle repeats until the goal is achieved or the agent determines the task requires a decision beyond its scope.
What are its key components?
Every AI agent is built from several layers:
The language model (LLM) serves as the reasoning engine — it interprets context, generates plans, and formulates instructions for external systems.
Memory operates at two levels: short-term (the current context of the conversation and task) and long-term (stored in a vector database — previous results, user preferences, domain knowledge).
Tools are the set of functions the agent can invoke: web search, code execution, sending emails, database queries, file operations, calls to external APIs.
The orchestrator manages the order of tool invocations, handles iterative loops, and decides when a task can be considered complete.
The human-in-the-loop (HITL) interface defines points where the agent must wait for user approval before executing an irreversible action.
In complex deployments, Multi-Agent Systems are used: a separate planning agent, an execution agent, and a verification agent work together, delegating tasks among themselves.
What can it be used for?
AI agent applications span nearly every sector with repeatable processes requiring data interpretation and decision-making.
In finance, agents analyse investment portfolio risk in real time, automate invoice processing, verify transaction compliance with regulations, and handle payment disputes.
In cybersecurity, systems such as Agentic SIEM/SOAR monitor network anomalies, correlate signals from multiple sources, and can autonomously isolate compromised assets — faster than an analyst can respond.
In sales and e-commerce, agents qualify leads, conduct initial correspondence with prospects, optimise SEO content, and manage supply chains.
In healthcare, they handle patient registration, analyse test results (under specialist supervision), and identify emergency cases through conversational interviews.
In manufacturing and IoT, they predict machine failures (predictive maintenance), optimise production line parameters, and manage inventory.
In office environments (Microsoft 365, Google Workspace), they automate document processing, report generation, and project management.
How does it differ from other approaches?
The fundamental difference between an AI agent and a classical chatbot (e.g., a basic version of ChatGPT without tools) lies in the direction of information flow and the degree of autonomy.
A chatbot operates on a question → answer schema. Each query is independent. The model has no access to external systems and takes no action beyond generating text. The entire burden of "guiding" the conversation and deciding what to do with the response falls on the user.
An AI agent operates on a goal → plan → multiple actions → result schema. It accepts a general task, breaks it down into steps itself, and executes them — including calling external systems and correcting the plan mid-execution. The user specifies the goal, not each individual step.
Compared to classical RPA (Robotic Process Automation) systems, an agent offers greater flexibility: RPA executes a pre-programmed script, while an agent can handle exceptions and unanticipated situations by interpreting context through the LLM.
Key limitations and challenges
Risk of cascading errors. A language model can make reasoning mistakes (hallucinations). In an agentic architecture, an error at one step can propagate through subsequent steps before being detected. The more autonomous the agent, the more serious the potential consequences.
Data quality. An agent's effectiveness is directly tied to the quality of the data it can access. An inconsistent or incomplete organisational knowledge base translates directly into incorrect decisions.
Infrastructure requirements. Production multi-agent deployments require substantial resources: GPU clusters, fast networks (100–400 GbE or InfiniBand) and a well-designed data architecture. On-premise deployment involves significant hardware investment.
Cybersecurity. The same capabilities that make agents useful in network defence can be exploited by attackers. Autonomous malware based on agentic architecture is a growing threat.
Alignment and ethics. Ensuring that an agent's goal remains aligned with organisational values and does not produce unintended side effects (the alignment problem) is one of the active areas of AI safety research.
Why does it matter?
AI agents represent a shift in the model of human–software interaction. For decades, interfaces were passive — they waited for commands. Agents invert this relationship: they take initiative, plan, and act.
The practical significance of this shift is visible in early deployments. Analysts at McKinsey estimate that agent-based automation could free 60 to 70 percent of the time knowledge workers spend on repetitive tasks. The first deployments in banking, logistics, and IT services are already producing measurable reductions in operational costs.
What is equally significant is what this technology means for IT system architecture. For years systems were built on point-to-point integration or middleware layers. Agents introduce a coordination layer based on natural language and reasoning — meaning that complex business processes can be described and modified without reprogramming workflows.
The risks are real and should not be minimised: absence of control mechanisms, poor data, excessive autonomy without human approval checkpoints — these are paths to serious failures. Responsible deployment requires building governance policies for agents in parallel.
For organisations, the question is no longer "whether to deploy AI agents" but "how to deploy them safely and with measurable value." That is precisely where we are in 2025.
Sources
- Oracle — What Is Agentic AI? — oracle.com
- Microsoft — Plan and implement responsible AI — microsoft.com
- SAP — Agentic AI in Enterprise — sap.com
- Trend Micro — Agentic AI in Cybersecurity — trendmicro.com
- LangChain — framework documentation — langchain.com
- Wikipedia — Intelligent agent — wikipedia.org