Simple reflex agents
React directly to what they sense using if-then rules, with no memory of past states. Fast and predictable — a thermostat or a spam filter.
Rule-basedA guide, updated by hand
What AI agents are, and why they matter
An AI agent is an autonomous software system that perceives its environment, reasons about a goal, and takes action to reach it — without waiting for a human at every step.
Understanding AI agents
An agent is more than a chatbot. It sets sub-goals, uses tools, remembers context, and takes multi-step action.
The four pillars of an agent
Perception
Takes in the environment — text, images, sensor readings, API responses, or a person's instruction.
Reasoning
A language model or decision engine interprets the input, breaks the goal into sub-tasks, and picks the next action.
Action
Does the work — browsing, writing code, calling APIs, sending email, driving other software.
Memory
Holds short-term context and long-term knowledge so decisions stay coherent across many steps.
How AI agents work
A continuous perceive–plan–act loop, with tools and memory carrying the work forward.
Step 1
Input
A goal arrives from a person or an event in the environment.
Step 2
Plan
Breaks the goal into sub-tasks and chooses which tools to use.
Step 3
Execute
Calls APIs, writes code, browses the web, changes files.
Step 4
Observe
Reads back the result and checks it against the goal.
Step 5
Respond
Returns the result, or loops again if the work is not done.
A simulated multi-agent network passing messages between specialised roles.
Types of AI agents
From simple reactive systems to self-learning networks, each suited to a different kind of task.
Model-based agents
Keep an internal model of the world so they can act in environments they cannot fully see. Common in robotics and navigation.
State-awareGoal-based agents
Reason about which actions lead to a desired outcome and plan a sequence of steps. Found in game-playing AI and route planners.
PlanningUtility-based agents
Choose the action that maximises a utility function, balancing objectives that compete. Behind recommendation engines and trading systems.
OptimisingLearning agents
Improve from experience through reinforcement or supervised learning, adapting to new environments without being reprogrammed.
AdaptiveMulti-agent systems
Networks of agents that collaborate or compete on problems too large for one. Used in supply-chain AI and autonomous vehicle fleets.
CollaborativeWhere you already meet them
From your morning alarm to enterprise automation, agents are already part of daily life.
Voice assistants
Siri, Google Assistant and Alexa understand spoken commands, manage calendars, control smart-home devices and answer questions without manual input.
Recommendation engines
Netflix, Spotify and YouTube run agent-style systems that learn your preferences and curate a personalised feed on their own.
Shopping agents
Price trackers, product suggestions and checkout assistants that remember what you like and watch for a better deal.
Navigation and autonomous vehicles
Route planners that re-route in real time, and self-driving systems that perceive the road and decide in milliseconds.
Email filtering
Smart compose, spam detection and priority inboxes learn from your behaviour to surface what actually matters.
Personal health agents
Wearables and health apps that track vitals, flag anomalies, prompt you to move, and escalate alerts when something looks wrong.
Customer service
Agents handle first-line support, route tickets, resolve common questions and escalate the hard cases, cutting response times to seconds.
Analysis and reporting
Query databases, build dashboards, surface anomalies and write the summary — a whole analytics workflow, automated.
Coding assistants
Tools like GitHub Copilot and Cursor work as pair programmers, writing, reviewing and debugging code from a developer's intent.
Document processing
Extract, classify and summarise contracts, invoices and reports — back-office work at a scale people cannot match.
Supply chain
Multi-agent systems that coordinate inventory, logistics and suppliers, adapting in real time when something breaks.
Cybersecurity
Monitor networks continuously, detect threats, isolate breaches and start countermeasures faster than any human team.
Agents in production
How leading organisations put agents to work outside the lab.
OpenAI Operator
Web automation · OpenAI
A computer-use agent that browses, fills forms, books travel and finishes online tasks on its own — general-purpose web agency at consumer scale.
AlphaFold and Med-Gemini
Healthcare · Google DeepMind
AlphaFold predicts protein structures and speeds up drug discovery. Med-Gemini works as a clinical reasoning agent on complex medical questions.
Palantir AIP
Enterprise · Palantir
Runs agents across defence and commercial operations — orchestrating data pipelines, generating reports and supporting decisions at machine speed.
Tesla Full Self-Driving
Automotive · Tesla
A real-time sensor stack that perceives the road, plans a route and controls the car, learning from edge cases collected across the fleet.
GitHub Copilot Workspace
Developer tools · GitHub
Reads an issue, plans the implementation, writes code across files, runs the tests and opens a pull request — end to end.
Amazon Robotics
Logistics · Amazon
Hundreds of thousands of robots in fulfilment centres act as coordinated agents, picking, sorting and routing packages through one orchestration layer.
How we got here
From rule-based scripts to large reasoning models, in seven steps.
1950
The Turing test
Alan Turing proposes a benchmark for machine intelligence, laying the conceptual ground for agents.
1966
ELIZA
Joseph Weizenbaum builds the first conversational agent at MIT — pattern matching that foreshadowed today's assistants.
1997
Deep Blue
IBM's goal-based chess agent beats Garry Kasparov, showing AI could outperform people in a constrained domain.
2011
Watson and Siri
Watson wins Jeopardy! and Apple ships Siri, putting agents into everyday consumer devices at scale.
2016
AlphaGo
DeepMind's reinforcement-learning agent beats the world Go champion, demonstrating superhuman strategic play.
2022
ChatGPT and the LLM era
Large language models become the reasoning engine for a new generation of agents.
2024–2026
Agents go mainstream
Claude, GPT and Gemini power autonomous systems that browse, code, plan and collaborate inside real workflows.
By the numbers
Figures behind the fastest-growing category in software. Check the source before citing.
0%
of enterprises deploying AI agents Gartner estimate
$0T
projected AI market size by 2030 Goldman Sachs
0K+
Amazon robots in operation Amazon
0M+
ChatGPT weekly active users OpenAI, 2025
0%
of developers using AI coding tools GitHub survey, 2025
Latest developments
Highlights from the frontier of agent research and deployment.
Anthropic releases the Claude Agent SDK for multi-agent pipelines
Developers can orchestrate fleets of specialised agents that collaborate on long-horizon tasks, from software engineering to research.
Read moreGemini agents reach expert level on medical benchmarks
Med-Gemini performs at specialist level on clinical reasoning tasks, a milestone for AI-assisted diagnosis.
Read moreCopilot Studio now powers 200,000+ enterprise agents
Microsoft reports enterprise adoption of autonomous agents has doubled year on year, covering HR, finance and legal workflows.
Read moreEU AI Act transparency rules for agents take effect
Businesses must now disclose when an autonomous agent handles a customer interaction, with accountability requirements for high-risk uses.
Read moreStanford AI Index tracks agent use in knowledge work
The annual index reports a sharp rise in the share of cognitive tasks handled by autonomous agents across surveyed enterprises.
Read moreOpen-source agent frameworks converge on a shared toolkit
Building, deploying and monitoring multi-agent systems is consolidating around a smaller set of well-supported libraries.
Read moreStart building with agents
The agentic era is here. Explore the frameworks, tutorials and tools behind these systems.