AI Agents Explained
- Priank Ravichandar
- Jan 13, 2025
- 4 min read
Updated: Oct 23, 2025
An overview of AI agents for product team members and stakeholders.
Key Takeaways
AI agents are goal-oriented systems that reason, plan, and act to achieve objectives.
They can execute complex, variable, multi-step workflows.
They complete tasks by following a Thought–Action–Observation loop.
Core components: LLM (brain), tools (body), and orchestration layer for coordination.
Single-agent vs. multi-agent: one generalist vs. multiple specialists working together.
AI Agents vs. AI tools: agents autonomously achieve goals; tools only perform specific tasks.
Overview
Note: Generated using NotebookLM using content from Hugging Face AI | Agents Course, Microsoft | AI Agents For Beginners, and Datacamp | Introduction To AI Agents.
Common FAQs
What are AI agents?
Agents are AI systems capable of reasoning, planning, and acting to achieve predefined goals.
An AI agent can think through a problem, plan the necessary steps, and take actions, interacting with external systems as needed. They possess agency to interact with the real world. These systems leverage an AI model (typically a Large Language Model) to execute workflows to complete various tasks. They can adapt to different scenarios, utilize external tools, perform multiple steps autonomously, or even delegate workflows to other agents. They are best suited for addressing complex problems that require flexible and adaptive problem-solving. They can determine which tools to use and how to use them to achieve the overall objective.
What can AI agents do?
AI agents offer a range of powerful capabilities:
They can analyze information and devise strategies to solve problems.
They can break down complex problems into smaller, manageable steps.
They can use tools to achieve objectives, even in unpredictable situations.
They can observe and understand the results of their actions, and adapt as necessary.
How do AI agents “think”?
AI agents operate using the Thought-Action-Observation (TAO) cycle.
Thought: The LLM decides the next step based on the user prompt and current situation. This involves internal reasoning and planning.
Action: The agent takes an action by calling available tools with associated arguments.
Observation: The model reflects on the response from the tool, feeding this information back into the next set of thoughts and actions. This loop continues until the objective is fulfilled.
What are the key components of an AI agent?
An AI agent consists of two main components:
The large language model (the brain) that handles reasoning and decision-making.
The tools (the body) it uses to interact with the external world.
The orchestration layer that governs how the agent processes information, plans, and executes actions to achieve goals.
Why does equipping agents with the right tools matter?
The tools an AI agent is equipped with determine the actions it can take.
An agent’s capabilities are restricted to the tools it can access. For example, if you ask a customer service AI agent about the weather, it won’t be able to fulfill that request because it has no tools to access weather data. Tools can take various forms, such as functions (custom code), data stores (databases or document archives), or extensions (connections to external APIs).
Do we need a single agent or multiple agents?
The choice depends on the complexity, predictability, and scope of the intended application.
In single-agent systems, a single AI model, equipped with tools and instructions, executes workflows in a loop to accomplish tasks. In multi-agent systems, multiple agents, each equipped with specialized tools and instructions, work together to execute workflows. Typically, a central agent delegates tasks to other agents, or agents themselves decide to hand off tasks to one another. AI systems break down complex requests into multiple distinct tasks. A single-agent system would have to reason, plan, and execute all the tasks in sequence. A multi-agent system can assign tasks to specialized agents, who can work in parallel to complete the tasks. Multi-agent systems can handle different aspects of a task more effectively than a single, generalist agent.
How are AI agents different from AI tools?
Agents autonomously achieve goals, while AI tools are used to efficiently complete tasks.
AI tools are task-oriented, excelling at performing specific, predefined functions. When someone uses an AI tool, they are likely to use it for a specific part of their workflow. For example, if you are solving a customer problem, you might use AI tools for several connected tasks (draft emails, summarize their feedback, etc.). However, you still need to think about how, when, and where to use tools to achieve your overall goals of helping the customer. The quality of the prompts entered can greatly influence their outputs.
In contrast, AI agents are goal-oriented, meaning they work toward achieving a broader objective. They can independently complete the series of tasks associated with what the user is trying to accomplish. They can make decisions and take action without constant human intervention. For example, AI agents might communicate with your customers, understand their problems, and interact with your systems. The scope and complexity of the tasks AI agents can handle on our behalf are much greater than what individual AI tools can do.
