What is an AI agent? Definition, examples, types, and how agentic AI works

Learn what an AI agent is, how it works, common agent types, and real-world use cases, plus how agentic AI differs from assistants, copilots, and chatbots.

What is an AI agent?

An AI agent is a system of connected software assets powered by Large Language Models (LLMs) that can access external tools to gather data, reason, and take action to meet a specified goal with little to no human interaction. 

Unlike passive chatbots, an AI agent can access external systems somewhat autonomously in order to act as a digital assistant capable of multi-step workflows. In equipping an AI agent with permissions for data discovery and access to external MCP servers, the ability to move beyond call-and-response AI and toward autonomous AI becomes a real possibility. 

AI agents vs. AI assistants: What’s the difference?

We’ve been using AI assistants across the globe for a couple of years now. Anthropic’s Claude, ChatGPT, Google Gemini, and other AI assistants are the LLM chatbots that have become ubiquitous across industries and applications. These AI assistants are capable of exactly that — assisting human users with simple tasks, such as discovering information, creating simple tables or infographics, or summarizing large batches of content. 

But these AI assistants are limited by the training data sets they have been fed. If asked for information outside of that training set, or to complete a multi-step task that requires access to an external tool or server, these AI assistants come up short. 

In contrast, an AI agent that has been paired with one or more MCP Clients and given access to one or more MCP servers can discover data beyond its original training data set and can use specified tools, resources, and protocols to complete multi-step tasks.

These autonomous AI agents are changing what is possible in AI, especially when it comes to harnessing the power of your data and paired with a data quality platform to insure the agent has correct and up-to-date information. 

Criteria	AI Assistant	AI Agent
Definition	Conversational AI that helps users complete tasks, answer questions, and generate content	Goal-oriented AI system that can plan, use tools, and take actions across multiple steps with limited human input
Primary interaction	Primarily user-driven through chat, voice, or embedded workflows	Task-driven and action-oriented, often operating across systems, tools, and data sources
Level of autonomy	Low to moderate, usually responds to direct prompts	Moderate to high, can pursue goals and execute workflows with less step-by-step guidance
Context retention	Limited session memory or short-term context, depending on implementation	Broader working memory with task state, history, and persistent context where supported
Proactivity	Mostly reactive, with occasional prompts or suggestions	More proactive, can make decisions, trigger workflows, and adapt next steps based on results
Multimodal capabilities	Can work across text, voice, and sometimes images or files	Can combine multimodal inputs with structured data, system signals, and tool outputs
Integration complexity	Moderate, often added to existing user workflows and applications	High, typically requires orchestration, access controls, monitoring, and governance
Ideal use cases	Research, drafting, summarization, Q&A, scheduling, and task support	Incident response, workflow automation, data operations, service orchestration, and complex decision support
Strengths	Fast assistance, strong language capabilities, and helpful user interaction	Multi-step execution, tool use, automation, and goal-driven reasoning
Challenges	Limited autonomy, inconsistent results with vague prompts, and dependency on available context	Higher operational risk, more complex governance, and greater infrastructure and oversight requirements

How do AI agents work?

AI agents work when connected to the systems and tools they need to complete the tasks required of them. Once granted the appropriate access and permissions, AI agents loop through four main actions: 

• Data discovery: AI agents identify what data they need and gather it from the sources necessary, such as databases, APIs, emails, or chat logs. 
• Reasoning and planning: The LLM behind the AI agent acts as a brain, which analyzes the data gathered in light of the goal, and then determines what steps are needed to reach that goal. 
• Take action and use tools: AI agents then use the external tools and APIs they have been given access to for task execution. 
• Reflect and learn: AI agents can use memory to learn from past interactions and improve their performance in future tasks.

In repeating these actions in a continuous loop, AI agents can work autonomously until a task is fully executed, needing no human input to move to the next step like with AI assistants. 

What is agentic AI?

Agentic AI is an autonomous AI system able to operate with little to no human oversight within previously established parameters. These agentic platforms can cycle through the four steps above — discovery, reasoning, action, and learning — with minimal human interaction, allowing them to complete tasks independently. 

In the real world, agentic AI makes automating end-to-end data pipelines possible. By employing a data quality AI agent across your enterprise, your data governance can be overseen, acted upon, and audited 24/7, reducing risk and shortening error resolution timelines. 

LLM agents explained: Why LLMs power most AI agents

An AI agent derives its power from a Large Language Model, and is therefore sometimes referred to as an LLM agent. These LLMs provide the brain of the AI agent, enabling it to plan and reason, and provide the language capability necessary for the AI agent to interact with a human user. 

While powered by LLMs, AI agents are not LLMs themselves. Instead, they harness the LLMs knowledge, resources, and language capabilities in order to take action. 

AI agents examples: Real-world use cases in the enterprise

What are AI agents able to do? There are endless real-world use cases for AI agents in enterprise settings: 

Customer support and employee help desks

AI agents are becoming valuable parts of customer support and employee help desks. AI agents can reduce error resolution times by handling data-heavy tasks like processing returns and answering FAQs, and they can do these things 24/7. If an AI agent lacks the resources necessary to handle an inquiry, it can escalate the issue to human attention. 

In addition to efficiency, AI agents can also provide more consistent responses to customer issues, as well as review interactions and gauge customer sentiment, helping to pull in human employees into situations where time-sensitive or emotionally-sensitive issues arise. 

IT operations and incident response

In the realm of IT operations and incident response, AI agents can reduce resolution times from hours to minutes. AI agents can detect and analyze issues and move to a resolution with minimal to no human interaction, creating a proactive ITops system management plan. 

When an AI agent is available to handle low-level interactions like requests for password resets and server reboots, IT departments can reduce the burden on employees and free them up for higher-level projects. 

Cybersecurity can also become more proactive with AI agents in the mix, since the AI agent can monitor and pinpoint unusual usages and begin to isolate servers and systems before human employees are even aware of the problem. The continuous oversight available through the use of AI agents in IT increases uptime and decreases manual error. 

Data workflows: data quality, governance, and lineage

When it comes to data quality, autonomous AI agents are increasing the capability of automating data workflows, increasing data governance, and reducing risk. 

With previous AI integrations in data observability, error detection was usually where artificial intelligence stopped being helpful. Human employees would get the error message and then begin the time-consuming task of figuring out why the error occurred and what downstream systems it might be affecting. 

With Ataccama ONE, data quality meets agentic AI. As opposed to just assisting with error detection, agentic AI can loop through discovery, planning, action, and reflection in order to resolve errors on its own. Unlocking agentic AI means that the end-to-end data observability platform from Ataccama that enterprises already trust is now powered by a built-in ONE AI Agent, helping teams reduce manual effort and time to resolution. 

Types of AI agents (from simple to advanced)

AI agents range from simple call-and-response models to constantly advancing agents capable of learning and reflection. From most simple to most advanced, here are types of AI agents and their pros and cons: 

• Simple Reflex Agents — Simple reflex agents respond instantly to external stimuli in an “if-then” format. Automated doors are one example: if the sensors detect movement, they open. These agents perform one task, and perform it with lightning-fast accuracy — provided the proper inputs are in place. They cannot reason, cannot make decisions, and cannot perform tasks that require memory storage. 
• Model-Based Reflex Agents — Model-based reflex agents take AI a step further. They can maintain an internal model of their environment, track the state over time, and infer missing information. With these skills, Model-Based Reflex Agents can react based on current and past contexts, like smart home security systems. 
• Goal-Based Agents — Goal-Based Agents are AI systems built to achieve a specific goal. They can plan, model possibilities, and optimize for outcomes. Vehicle GPS systems are goal-based agents: they calculate routes from point A to point B, while taking in real-time data on accidents or closed intersections to reroute you based on current traffic conditions. 
• Utility-Based Agents — Utility-Based Agents take goal-setting a step further, operating with a goal in mind, but the capability to optimize for how well the goal is met by balancing conflicting factors. These agents are how some vehicles can evaluate routes and optimize for both speed and fuel efficiency, instead of just shortest distance to end. 
• Learning agents — Learning agents are currently the most complex AI agent available. Over time they learn from data, resources, and past performance in order to improve with each task execution. These are the types of agents that are making truly agentic AI possible. 

Autonomous AI agents: Benefits, risks, and guardrails

The benefits of autonomous AI agents include increased efficiency, reduced downtime, and end-to-end data pipeline visibility. 

There are also some risks in using autonomous AI and some guardrails to consider when implementing artificial intelligence into your enterprise. 

Here are a few of the potential risks when implementing autonomous AI: 

• Data Manipulation: AI agents can be tricked or manipulated by feeding them incorrect data that affects decision-making. 
• Increased Bias: If unfairness exists within training data, AI agents can magnify it resulting in automated decisions that harm specific customer segments. 
• Unauthorized Autonomous Action: Agents can destroy data or modify it without human authorization. 
• Data Breaches: If proper controls aren’t built in, AI agents can release sensitive data or create data violations at scale. 

Common mitigation strategies and guardrails for using AI agents can include: 

• Access Controls: Providing strict access controls up-front, like role-based access, can reduce AI agents from having escalated privileges over time. 
• Company-Wide Governance Plans: Create and maintain AI frameworks with continuous monitoring, as well as automated reporting. 
• Require Human Oversight: Use Human-in-the-Lopp (HITL) controls for decisions like data destruction or overrides, ensuring that a human employee ultimately is responsible for high-impact decisions. 

Guardrails for AI agents are essential to manage the flow of data at all execution points, reducing risk and increasing security. 

How to build and deploy AI agents: A practical checklist

When building an AI agent for enterprise, there are a number of considerations to be made. Before you develop and deploy an AI agent, you need to know what your goals are, have a security plan in place, and know what success metrics look like for your AI agent strategy. 

Here are a few steps to consider before you deploy an AI agent for your data management system: 

Step 1: Define goals, boundaries, and success metrics

When implementing an AI agent, you must first define the goals that are important to your organization. Is the goal increased productivity? Reduced error? Optimization for a specific part of the business? A combination of all three — or something else entirely? 

Once you have your goals in mind, you must consider what boundaries and guardrails you need to create and enforce in regards to your AI agent. What systems will the AI agent have access to? Will you use rule-based boundaries? Will you have any automated server shutdowns in place in case an AI agent begins to escalate its own access? How will you isolate your servers from one another in case of emergency? Working with your IT operations and compliance departments at the beginning of the process in deploying an AI agent is essential for ensuring risk mitigation. 

Another thing to consider is what success metrics you will use to grade your AI implementation. Cost savings, operational efficiency, and error resolution times are just a few of the options. 

Step 2: Choose tools and data (and govern access)

Once you’ve defined your goals, boundaries, and success metrics, take a look at what tools and data you will give an AI agent access to. Your IT team can isolate certain servers to keep them from ever being accessed, and compliance teams can advise on which data should be siloed away from AI privileges. 

Best practice for AI agent use is the Least Privilege Principle: give the AI agent the least amount of access possible to enable it to do its assigned task. Once you’ve identified what that Least Privilege is for your situation, create a structured framework that includes policies, IT controls, and monitoring and auditing of AI agents to ensure they are staying within authorized bounds.

Step 3: Test for reliability, security, and hallucinations

Setting up an AI agent with the proper tools and access governance isn’t enough: you must also test that AI agent to ensure that it is operating reliably and securely. This requires a layered approach to combine testing for the physical hardware, scenario-based tests, and constant monitoring to ensure that the AI agent continues operating correctly and safely. Ensuring that your AI agent can handle malformed data and can avoid access escalation is a must before deploying in your system. AI hallucination testing needs to ensure the model is providing accurate and consistent outputs and protecting against false data. 

Step 4: Monitor in production and continuously Iimprove

The last step for AI agent deployment is not a last step at all: it’s that you continuously audit your AI agents and work toward improvement in the agent itself and in the way it interacts with your system and aids your use case. AI agents must adapt to new environments and new data, and the guardrails around those AI agents must adapt as well. 

Deploying an AI agent is not a “set it and forget it” scenario, but a process that will be constantly retooled and refined as new use cases are discovered. Building and deploying an enterprise AI agent can take a lot of internal and external resources, and the landscape of artificial intelligence is constantly changing.

FAQs

• What is an AI agent?
  • An AI agent is an autonomous AI system that can discover data, plan, reason, and execute tasks to achieve a specific goal with little or no human interaction. 
• How do AI agents work?
  • AI agents work by connecting LLMs to tools and resources necessary to complete multi-step tasks using four repeated actions: data discovery, reasoning and planning, tool and resource execution, and reflection and learning. 
• What is agentic AI?
  • Agentic AI is an autonomous AI system that can operate with minimal human oversight within given parameters. Agentic AI can think, reason, and take action to complete tasks independently. 
• What are some real-world AI agent examples?
  • AI agents in the real world can streamline customer service interactions, create proactive IT management plans, monitor data pipelines, and more.