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An AI agent is a software system that does more than answer questions — it understands a goal, plans the steps required, and takes actions to achieve that goal. Think of an AI agent as a proactive digital assistant: it can reason about a task, decide the next steps, and execute actions rather than only providing instructions. The defining characteristic of an AI agent is autonomy. Unlike basic AI applications that require explicit, step-by-step commands, an autonomous agent determines the next actions it needs to take to reach a goal. High-level agent loop:
  • Reason: Interpret the user’s goal and derive sub-steps.
  • Decide: Evaluate options and select the most appropriate next action.
  • Act: Use tools and integrations to perform actions that move toward the goal.
  • Reasoning: The agent analyzes the user’s objective and identifies necessary sub-tasks. For example, when asked “Find me a suitable time with my team,” an agent infers participants, inspects calendars, and determines availability windows.
  • Decision-making: If multiple valid actions exist, the agent selects the best one given constraints, priorities, and context (e.g., picking one of several candidate meeting times).
  • Performing actions: The agent performs real-world tasks by calling APIs or tools. Instead of saying “schedule a meeting,” it can use a calendar API like Outlook or Google Calendar to create the event, or call a database to fetch or update records.
Remember the core flow: Reason → Decide → Act. This sequence is what gives an agent practical autonomy to pursue a user goal.
A slide titled "What Are AI Agents?" showing a robot icon labeled "Agent." An arrow points to a three-step flow under "Autonomous systems" numbered 01–03: Reason, Make decisions, and Perform actions.

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Core building blocks of an AI agent

An agent combines several components so it can reason, act, and stay aligned with policies and data. These building blocks are typically modular and work together in production systems. These components let the model reason and produce language, keep behavior aligned with instructions, allow tools to perform actions, and ground results with accurate knowledge.
A slide titled "What Are AI Agents?". It lists components an agent uses — Models, Instructions, and Tools — with short bullet points, plus a Knowledge section.

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Collaboration patterns: people and multi-agent workflows

Agents rarely operate alone. They can work directly with users or coordinate with other agents in multi-agent systems. Agent + User collaboration
  • The user provides a goal (e.g., “Prepare for tomorrow’s customer meeting”).
  • The agent gathers documents, summarizes key points, answers follow-up questions, and — if authorized — performs actions like creating an agenda or scheduling the meeting.
  • This model increases user productivity by letting the user describe outcomes instead of enumerating steps.
Agent + Agent collaboration
  • For complex or specialized tasks, multiple agents can coordinate.
  • Each agent focuses on a domain (e.g., data analysis, financial calculations, report generation). Agents exchange intermediate results and combine their outputs to solve the larger problem.
  • This multi-agent pattern avoids monolithic designs and enables scalable specialization, similar to a cross-functional team.
A slide titled "What Are AI Agents?" showing a central robot-like "Agent" that connects users (who use agents to assist with tasks, answer questions, and improve productivity) with other AI agents (enabling multi-agent workflows, specialized coordination, and solving complex problems). The diagram highlights collaboration between users, a single agent, and other AI agents.
Agents that perform external actions must be designed with access controls, auditing, and data handling policies. Limit permissions to the minimum necessary and ensure sensitive data is never exposed unintentionally.

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Summary

An AI agent acts as a bridge between human goals, AI capabilities, and external systems. It accepts a user goal, reasons about the task, chooses and executes actions via tools, consults knowledge sources for accuracy, and coordinates with people or other agents when needed. Well-designed agents strike a balance between autonomy, safety, and practical usefulness.

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