What is an AI Agent?
- IAM Glossary
- What is an AI Agent?
An AI agent is a Non-Human Identity (NHI) that can perceive or receive signals about its environment, make decisions and take actions (often via tools or integrations) to achieve specific goals. It uses Artificial Intelligence (AI) techniques, including Large Language Models (LLMs), machine learning models and rule-based logic, to analyze data, determine next steps and complete tasks with varying levels of human intervention. Unlike standalone, single-turn AI models that primarily generate responses to prompts, AI agents are goal-oriented and action-driven, and they can operate over multiple steps by maintaining state, using tools and adapting to new contexts.
AI agents vs chatbots vs generative AI
Although they are often used interchangeably, AI agents, chatbots and generative AI differ in function and level of autonomy. AI agents are designed to make decisions and take actions to achieve certain goals, often integrating with external systems to execute workflows. Chatbots mainly conduct conversations in response to prompts, but some modern chatbots can perform limited, predefined actions. Generative AI focuses on creating novel outputs (i.e., text, code, images or structured data) from learned patterns and may be used within automated systems, but on its own, it does not necessarily plan or execute real-world actions. Here are their main differences:
| Feature | AI agents | Chatbots | Generative AI |
|---|---|---|---|
| Function | Achieve goals through decision-making and autonomous actions | Primarily conduct conversations and respond to queries | Generate content based on prompts |
| Level of autonomy | Can plan and execute tasks with minimal human input | Reactive based on user input | Produces outputs when prompted (autonomy depends on how it's integrated) |
| Ability to take action | Can execute workflows and call APIs | Some can perform predefined actions | Typically produces outputs; it only “takes action" when connected to tools or systems |
How AI agents work
AI agents function in a repeating, iterative cycle of perception, decision-making and action. Although the technology behind AI agents can vary, most follow a similar workflow:
Receives an instruction, prompt or goal: The AI agent is triggered by a user request, predefined objective or event-driven trigger from a system.
Gathers context: Before it can complete its task, the AI agent collects relevant data such as user input, database records or API responses. Some agents maintain short-term or long-term memory to reference past experiences or prior interactions.
Makes a decision: Using machine learning models, LLMs or rule-based logic, the AI agent assesses possible actions, weighs potential outcomes and determines the best next steps.
Plans and executes actions: For complex objectives, the AI agent establishes a sequence of actions, like updating files or systems, to achieve its goal.
May evaluate results: Some advanced agents can evaluate whether their action achieved the intended outcome and then modify their future behavior accordingly.
Five main types of AI agents
AI agents can be categorized into five main types based on how they make decisions and interact with their environment.
1. Simple reflex agents
Simple reflex agents are the most basic type of AI agent, responding to specific inputs using predefined rules. These agents do not retain memory or consider broader context, making them ideal for predictable environments but limited in complex scenarios.
2. Model-based reflex agents
Model-based reflex agents maintain an internal model of their environment, allowing them to track changes and account for how past interactions affect present conditions. They are more adaptable than simple reflex agents because they remember aspects of previous states, but they still rely mostly on predefined logic.
3. Goal-based agents
Goal-based agents operate with a clear outcome in mind and determine which actions will help them achieve that goal. These agents consider future consequences and plan steps accordingly, but are still limited by their programmed decision frameworks.
4. Utility-based agents
Utility-based agents measure and compare the desirability of various outcomes, selecting the action that maximizes benefits and minimizes risk. Effective in dynamic environments, these agents weigh trade-offs and make more nuanced decisions than goal-based agents.
5. Learning agents
Among AI agents, learning agents are the most advanced because they continually improve their performance based on new data and past experiences. Their ability to adapt makes them ideal for complex environments.
Examples of AI agents
Across many industries, AI agents are used to automate decision-making, execute workflows and reduce human intervention. The following are several common examples of AI agents in real-world use cases:
- Customer support agents: AI-powered customer support agents analyze customer inquiries, retrieve account information, process refunds or escalate tickets to appropriate team members.
- Coding agents: Coding agents support developers by generating, testing and improving code, identifying vulnerabilities and suggesting improvements across code repositories.
- Autonomous vehicles: Self-driving vehicles use sensors and AI models to interpret road conditions and traffic patterns, and make real-time driving decisions for more efficient transportation.
- Inventory management agents: In retail environments, AI agents can monitor inventory levels, automate purchase orders, detect equipment malfunctions, reroute shipments to accommodate delays and reduce supply chain disruptions.
- IT operations agents: AI agents can be used in enterprise IT environments to detect anomalies, deploy patches and trigger predefined remediation workflows.
- Financial trading agents: In financial services, AI agents can analyze market data in real time and execute trades based on predefined strategies, prioritizing speed and scalability.
How AI agents introduce new security risks
As AI agents become more integrated into enterprise systems, they introduce new security concerns. Because AI agents can access data, interact with applications and execute actions autonomously, they can function as NHIs when they use their own identities, such as service principals, workload identities, API tokens or OAuth applications. Like service accounts or bots, AI agents operate with their own credentials and permissions, which means they must be carefully controlled and monitored in the same way as human users (i.e., least privilege, strong authentication and audit logging). In zero-trust security models, no identity — neither human nor machine — is implicitly trusted, and access must be continuously verified and authorized to reduce security risks. The main risks associated with AI agents include:
- Unauthorized access: Often, AI agents require credentials, tokens or API keys to work. If these secrets are exposed or misconfigured, cybercriminals may gain access to critical systems or internal databases through the AI agent's identity.
- Privilege escalation: When AI agents are granted excessive permissions, they may act beyond their intended scope. If compromised, an agent could change critical systems, access restricted data or modify configurations.
- Prompt injection attacks: AI agents that are powered by LLMs may be susceptible to prompt injection, where malicious inputs manipulate the system's instructions. These attacks can cause AI agents to leak sensitive information, expose credentials or execute unauthorized actions.
- Data leakage: Since AI agents typically connect to SaaS platforms and internal databases, misconfigurations can expose Personally Identifiable Information (PII) or confidential financial records.
- Over-permissioned integrations: To automate workflows, AI agents often integrate with multiple services and applications. When they're granted broad access across systems, compromised or misconfigured AI agents can significantly widen an organization's attack surface.
- Tool/API abuse and action integrity: Even without a “compromise," an agent can take incorrect but plausible actions, such as deleting data, emailing the wrong recipient or changing configurations, if it misunderstands context or receives ambiguous instructions.
- Supply-chain and dependency risk: Agents often rely on third-party models, plugins, connectors or tool servers; weaknesses or malicious updates in those dependencies can introduce new attack paths.
