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What are AI Agents? - Explanation & Meaning

Learn what AI agents are, how autonomous AI systems work, and why 57% of enterprises deploy them in 2026. Discover multi-agent systems and the MCP protocol.

Definition

AI agents are autonomous software systems that use an LLM as their core reasoning engine to independently plan, execute, and complete tasks, including invoking external tools and data sources.

Technical explanation

AI agents combine the reasoning capabilities of LLMs with the ability to take actions in the outside world. An agent receives a goal, breaks it into subtasks via a planning phase, executes each step by invoking tools (APIs, databases, web searches), and evaluates results to determine whether the goal has been achieved. The ReAct pattern (Reasoning + Acting) is the dominant architecture, where the model alternates between reasoning and acting. Multi-agent systems deploy multiple specialized agents that collaborate: an orchestrator agent distributes tasks to specialist agents for code, research, or data processing. In 2026, 57% of enterprise companies have implemented AI agents. Anthropic's Model Context Protocol (MCP) has emerged as the open standard for tool use, enabling agents to connect to data sources and services via a standardized interface. Frameworks such as LangGraph, CrewAI, and AutoGen facilitate development of complex agentic workflows with built-in error handling, memory, and human approval steps.

How MG Software applies this

MG Software develops AI agents that automate business processes for our clients. We build agents that independently collect data, generate reports, handle customer queries, and orchestrate workflows. Via the MCP protocol, we connect agents to CRM systems, databases, and internal tools so they operate securely and reliably within existing IT infrastructure.

Practical examples

  • A financial institution deploying AI agents to automatically generate compliance reports by collecting data from multiple internal systems, analyzing it against regulations, and compiling a report for compliance officer review.
  • An IT helpdesk using a multi-agent system where a triage agent classifies incoming tickets, specialist agents propose solutions, and a communication agent keeps users informed of progress.
  • An e-commerce company deploying an AI agent to automatically update product catalogs by scraping supplier information, comparing prices, and synchronizing inventory levels.

Related terms

model context protocollarge language modelragprompt engineeringartificial intelligence

Further reading

What is the Model Context Protocol?More about LLMsWhat is RAG?

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Frequently asked questions

A chatbot responds to individual messages without long-term planning or the ability to perform external actions. An AI agent, in contrast, can independently plan and execute complex tasks, invoke tools, look up information, and take multiple sequential steps to achieve a goal. An agent has agency — the capacity to autonomously make decisions and act.
With proper safeguards, yes. Best practices include: human-in-the-loop approval steps for critical actions, strict permission boundaries defining which tools an agent may use, comprehensive logging of all agent actions, and rollback mechanisms. The MCP protocol provides standard security layers for tool invocations.
According to research, 57% of enterprise companies have implemented at least one AI agent in their business processes in 2026. The most common applications are customer service automation, data analysis and reporting, and IT operational tasks. This percentage is expected to exceed 75% in 2027.

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