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AI agents are autonomous software entities that perceive, plan and act continuously to accomplish tasks. Used as an architectural pattern for assistants, automation and distributed multi-agent systems, they define interaction models and lifecycle concerns. This concept outlines design choices such as modularization, state management, security and integration…
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An AI agent is a software actor that does not just answer questions; it advances tasks on its own through perception, planning, and action.
The concept builds on research into intelligent agents and agent-oriented computing: systems should perceive their environment, pursue goals, and choose actions autonomously. FIPA later standardized agent architectures, and LLM-based implementations turned the pattern into a practical design for assistance, automation, and coordinated multi-step work in software platforms.
Think of an agent as a control loop around a task. It reads inputs and context, updates its state, plans the next step, calls tools or APIs when needed, and checks the result. The loop continues until the goal is reached, uncertainty becomes too high, or a stop rule applies. What matters most is the separation of model, memory, and tool layer, plus clear limits on permissions and responsibility.
The agent chooses intermediate steps on its own instead of receiving every action explicitly.
Inputs are interpreted, turned into a next step, and executed as an action.
Context, goals, and intermediate results remain available across multiple steps.
The language model often serves as the reasoning and control component for interpretation, inference, and planning.
The Model Context Protocol exposes external resources and tools through a standardized integration layer.
Agents act outward through browsers, databases, internal services, or other software interfaces.
Rules limit what the agent may read, change, or execute.
The pattern is useful when work spans several steps, external tools are required, and decisions cannot be fully pre-scripted: for support assistants, research, or workflow automation. Its value rises with clear interfaces, observability, and stop rules; without them, cost, latency, state drift, and security risks increase, including wrong or unwanted actions.
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