What is an AI agent?

AI agents represent a fundamental shift in how software interacts with the world. They think, plan, and act autonomously—pursuing goals strategically, adapting to unexpected challenges, and learning from each interaction. But unlike Mr. Smith from 'The Matrix', AI agents are designed to be helpful and efficient.

What is the difference between AI agents, AI assistants, and bots?

It's easy to mix up terms like "AI agent," "AI assistant," and "bot"—but each means something distinct, especially when building solutions for business or everyday use.

AI agents are autonomous systems that can make decisions, set goals, and act independently.
AI assistants are specialized agents focused on helping people—they understand human language, respond to requests, and often provide recommendations. Critically, they empower the user to make the final decision or take an action, rather than acting entirely on their own.
Bots are simple automated programs that follow pre-defined rules to perform repetitive tasks. They typically lack advanced reasoning or learning capabilities, and operate within a narrow scope—such as answering FAQs, delivering notifications, or processing basic commands.

	AI Agent	AI Assistant	Bot
Purpose	Acts on its own goals	Helps user on request	Does simple tasks only
Autonomy	High	Medium	Low
Capabilities	Learns, adapts, decides alone	Gives info, helps, suggests	Follows rules, limited learning
Examples	Self-driving car, DevOps agent	Siri, Alexa	Chatbot, FAQ bot

What are the different types of AI Agents?

AI agents can be categorized into several types based on their complexity and capabilities. Understanding these categories helps clarify how different agents operate and what they can achieve.

Simple reflex agents:
These agents take snapshots of the current situation using their sensors and only react if certain conditions are met. They don't have any memory, nor do they interact with other agents. One example is a thermostat: if the temperature exceeds 25°C, then turn on the AC.
Model-based reflex agents:
Model-based agents behave similarly to simple reflex agents, but additionally hold a memory and maintain an internal model of the world. Whenever they receive new information, they update their memory. Robot vacuum cleaners are model-based reflex agents, as they react to certain conditions sensed by their sensors (e.g., bumping against an obstacle, then turning left) and simultaneously keep an internal map of where they have already cleaned, so they don't repeatedly clean the same areas.
Goal-based agents:
Goal-based agents have an internal model of the world as well as a goal or set of goals. They are more effective than simple or model-based reflex agents because they first search for action sequences and then plan these actions before acting on them.
One example is a navigation system that searches for the fastest route to your destination. If a quicker route is found, the agent recommends that one instead.
Utility-based agents:
These agents select a sequence of actions that reach a goal while maximizing utility. Utility is calculated through a utility function, which assigns a utility value to each scenario based on a set of criteria.
For example, a navigation system that recommends a route that optimizes fuel efficiency while avoiding high traffic. The criteria are fuel efficiency and low traffic, so the route with the highest scores on those criteria has the highest utility.
Learning agents:
Learning agents are unique in their ability to learn. They have the same capabilities as the other types but, in addition, update their knowledge base whenever they experience something new. This greatly enhances their ability to operate in unfamiliar environments.
A perfect example is personalized recommendation systems on e-commerce sites. The more you interact with the website, the more information they store in their memory, and they use this data to improve recommendations. Their accuracy in predicting what you like grows with the amount of data they have.

How does an AI Agent work?

Usually, there are four main steps that AI agents loop through:

Goal setting: Using natural language (e.g., English), the user prompts the AI agent to complete a task.
Reasoning & planning: The AI agent interprets the prompt and builds a work plan.
Execution: Information is gathered and tools are used. In some cases, there might be multiple "sub-agents" assigned specific tasks by a manager agent. At the end of this step, the agent shares the draft output with the user.
Reflection: The agent receives feedback and iterates accordingly. The memory and knowledge are updated during this step as well.

Use cases for AI agents

Below are three typical examples of how AI agents can be used, though their applications span a wide range of industries. ¹

Customer agents
These agents enhance customer interactions by learning individual preferences, responding to inquiries, resolving problems, and suggesting suitable products or services. They can be incorporated into user experiences across chat, voice, or video channels.
Employee agents
Employee agents help increase efficiency within organizations by automating routine processes, handling repetitive tasks, providing answers to internal questions, and assisting with editing or translating critical documents and communications.
Data agents
Data agents specialize in advanced data analysis—they can uncover valuable patterns and insights from large datasets while ensuring accuracy and reliability in their findings.

How Lenovo is using agentic AI

Lenovo has implemented AI agents primarily in software engineering and customer support. According to Arthur Hu, CTO of Lenovo’s Solutions and Services Group, software engineering teams have experienced productivity improvements of up to 15 percent. Linda Yao, Lenovo’s COO and head of strategy, reports that customer service has achieved double-digit gains in call handling efficiency. ²

Benefits of AI agents

AI agents provide several valuable benefits for businesses and individuals.

Autonomy: After setting a goal, agents can plan, carry out, and improve their methods on their own. This means there is no need for constant reminders.
Scalability: AI agents can handle large volumes of tasks while maintaining the same efficiency and consistency.
Adaptability: Unlike fixed automation, agents continually enhance their performance by using feedback and changing their strategies.
Integration: AI agents can connect with external APIs, browse the internet, and use various digital tools to get current information and complete tasks.

Risks and limitations

While AI agents provide significant benefits, it's important to recognize their potential risks and limitations when using them in production environments.

Hallucination: AI agents might produce incorrect facts or take inappropriate actions due to the limits of language models. These mistakes can be subtle and hard to spot, possibly leading to misleading information or decisions.
Infinite loops: Agents may get stuck repeating pointless steps without making progress toward their goals. Without proper safeguards, they can waste resources indefinitely without delivering meaningful results.
Security & compliance: There are inherent risks of data leaks when agents interact with external systems or handle sensitive information. Proper access controls and monitoring are crucial to protect confidential data and meet regulatory requirements.
Cost: AI agents can require considerable computing resources, resulting in high token usage and related costs. Complex tasks that need multiple iterations or tool calls can quickly drive up expenses.
Debugging: Finding errors in agent behavior can be tough since their decision-making involves multiple steps, tool calls, and chains of reasoning. This complexity makes it hard to pinpoint the root cause when issues arise.

Best practices

To reduce risks and improve the effectiveness of AI agents, organizations should follow several important best practices.

Human-in-the-loop
Set up approval workflows for high-risk actions. This ensures human oversight before making critical decisions. It prevents agents from making harmful choices that could impact business operations or customer relationships.

Activity logging
Keep detailed audit trails of all agent activities, decisions, and interactions. This promotes transparency and accountability. Detailed logging helps organizations track agent behavior, identify problems, and show compliance with regulations.

Tool sandboxing
Limit agent access to external systems and APIs by using carefully controlled sandboxed environments. This reduces possible damage from unauthorized actions and keeps agents from accessing sensitive data or systems beyond their intended purpose.

Rate limits
Set usage caps and rate limits to control token use and avoid excessive costs. By monitoring and managing resource use, organizations can keep expenses in check while ensuring agents stay within budget limits.

Version pinning
Lock specific model versions to keep agent behavior consistent and predictable across deployments. This stops unexpected changes from model updates that could introduce new bugs or change agent performance.

Red-teaming
Regularly conduct security testing to find potential vulnerabilities, jailbreaks, or attacks that could threaten agent safety. Systematic testing helps organizations uncover and fix security weaknesses before they can be exploited in production environments.

Image: Agent Smith from 'The Matrix'

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