Quick Answer
An AI agent is software that uses a large language model to plan and execute multi-step tasks autonomously. Unlike a chatbot that only answers questions, an agent uses tools: web search, code execution, file systems, APIs, and browser automation to take real actions in the world. You give it a goal; it figures out the steps to achieve it. AI agents are the main direction of AI development in 2025-2026.
Dark mechanical spider hub with six copper-tipped arms radiating outward: an orchestration centre routing tasks to multiple tools and specialised agents.
An AI agent sits at the centre of a network of tools, routing tasks outward and synthesising results back into a coherent action plan.

Chatbot vs agent: the key difference

A chatbot has one turn: you ask, it answers. The conversation may continue, but each response is independent of anything outside the conversation window.

An agent has a loop:

  1. You give a goal: “Research the top five competitors to our product and write a brief report”
  2. The agent plans: “I need to search for competitors, then analyse each one, then write the report”
  3. The agent acts: searches the web, reads pages, extracts data
  4. The agent evaluates: “Did I find enough? Are there gaps?”
  5. The agent acts again: searches for missing data, fills in gaps
  6. The agent delivers: produces the report

The agent decides what steps to take, executes them using real tools, evaluates the results, and iterates. You can be away from the keyboard while it works.

The structure of an AI agent

Goal input
Natural language task System prompt Context documents
LLM (the brain)
Planning Tool selection Result evaluation Response generation
Tools (the hands)
Web search Code execution File read/write API calls Browser automation
Memory
Conversation history (short-term) Vector database (long-term) External state store
Output
Files created or modified APIs called (emails sent, records created) Reports and summaries

A concrete example: the research agent

Task given to the agent: “Find the last five press releases from our top three competitors and summarise what they are announcing.”

The agent’s execution:

  1. Calls web search tool: “press releases Competitor A 2026”
  2. Reads the top results, extracts press release text
  3. Calls web search tool: “press releases Competitor B 2026”
  4. Reads results
  5. Calls web search tool: “press releases Competitor C 2026”
  6. Reads results
  7. Calls code execution tool: Python script to sort by date and filter last five per company
  8. Generates structured summary from all gathered data

The LLM decides each step. It interprets search results, realises when it needs more data, and knows when it has enough to write the final summary.

The agentic loop

1 Observe The LLM reads the current state: the goal, conversation history, previous tool results, and any available context documents.
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2 Plan The LLM decides what to do next: which tool to call, with what inputs. This decision appears as a structured tool call in the model output.
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3 Act The tool is executed: a web search returns results, code runs and returns output, an API call returns a response.
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4 Evaluate The LLM reads the tool output. Did this move toward the goal? Is the task done? What is missing? Loop back to step 1 or deliver the final result.

Real-world agent applications in 2026

Use caseWhat the agent doesTools used
Customer supportReads ticket, queries CRM, drafts reply, escalates if neededCRM API, email API, knowledge base
Code reviewReads PR, runs tests, checks style, posts review commentsGitHub API, code execution
Research assistantSearches web, reads papers, extracts data, writes reportWeb search, file reader, summarisation
Data pipelineReads new files, transforms data, writes to database, sends alertFile system, SQL, Slack API
Sales outreachFinds prospects, personalises emails, sends at optimal timeCRM, email, web search

Frameworks for building agents

FrameworkLanguageBest for
Claude Code / Claude Agent SDKAny (SDK: Python, TypeScript)Coding tasks, file operations
LangGraphPythonComplex stateful agent workflows
CrewAIPythonMulti-agent collaboration
Microsoft Agent FrameworkPython, .NETMulti-agent workflows (successor to AutoGen, which is now in maintenance mode)
Strands AgentsPython, TypeScriptAWS-native agent workflows
Amazon Bedrock AgentCoreAnyManaged runtime, memory, identity and tool gateway for agents at enterprise scale

The older Amazon Bedrock Agents service was renamed Bedrock Agents Classic and closed to new customers on 30 July 2026 (maintenance mode); AWS recommends AgentCore for new builds.

Risks and design principles

Irreversibility: Agents can take actions you cannot undo (sending emails, deleting files, making purchases). Design agents to ask for confirmation before irreversible actions.

Error propagation: Mistakes in step 3 can cascade through steps 4, 5, and 6. Use checkpointing: save intermediate state and allow resumption from a checkpoint on failure.

Scope creep: Agents given broad goals may take unintended actions. Constrain the action space: define exactly which tools are available and what they can do.

Cost: Each tool call and LLM inference costs money. A 50-step agent run on a mid-tier model (at the time of writing, September 2026, for example GPT-6 Sol or Claude Sonnet 5) might cost €0.50-5. Profile before deploying at scale.

What’s next

Further reading