What Are AI Agents? Here’s What You Need to Know

What Are AI Agents, and Why Is Everyone Talking About Them?

AI agents are quickly becoming one of the most important concepts in enterprise AI, yet most explanations are vague, overly technical, or overly hyped.

Organizations are no longer just experimenting with passive generative AI. They are deploying agents into real workflows that can retrieve knowledge, update records, trigger actions, and interact with other software. In short, AI agents are now part of the enterprise’s execution layer.

That shift matters because the same capabilities that make agents useful also make them harder to govern. Once an agent operates with autonomy, memory, and access to connected tools, security must move beyond prompt filtering into runtime governance. Understanding where the market is heading starts with a strong foundation: what an AI agent is, how it works, what it’s used for, and why securing it matters.

What Is an AI Agent? A Definition

A simple definition: an AI agent is a software system that can interact with its environment, collect and interpret data, and use that data to pursue goals and complete tasks with some degree of autonomy.

An AI agent does more than generate text. It can gather inputs, choose from available tools, decide on next steps, execute actions, and adapt as the task changes. In practice, that might mean pulling information from a knowledge base, comparing multiple records, drafting a response, updating a ticket, sending a follow-up, and triggering a workflow in another application.

This is what sets an AI agent apart from a basic assistant. Instead of only answering prompts, it operates within broader workflows. Agents don’t just respond; they observe, reason, and act. That is what moves them from helpful assistants into something more powerful, and potentially more sensitive, inside the enterprise.

AI Agents vs. AI Chatbots

AI agents and chatbots can look similar on the surface. Both often use natural language, may appear inside a chat interface, and can answer questions. But they are not the same thing.

A traditional chatbot is mostly reactive. It waits for a prompt, generates an answer, and stops. An AI agent is more dynamic. It can work through a multi-step goal, use tools, maintain context, and take action across systems.

This difference becomes clearest in enterprise settings. A chatbot may answer a support question; an AI agent may classify the issue, retrieve account history, update the record, draft a response, and escalate the case if it meets specific conditions. A chatbot may explain a policy; an agent may retrieve the policy, compare it to a customer’s situation, flag an exception, and route the case for review.

That added capability is exactly what makes agents more valuable and more sensitive from an agentic AI security standpoint. Once software can act rather than just answer, oversight has to move closer to execution.

How Do AI Agents Work?

It helps to think of agents as moving through a loop: observe, plan, and act.

Observe: The agent gathers context from a user prompt, internal knowledge, files, databases, applications, logs, emails, tickets, or other connected systems.

Plan: It reasons through the goal, identifies required tools, and sequences next steps. This goes beyond pattern-matching a response. It’s active decision-making.

Act: It calls an API, retrieves data, updates a field, routes a ticket, generates a draft, or passes work to another system or sub-agent. Then it observes again and continues until the task is complete or escalated.

Many AI agents also rely on memory or state persistence, carrying context across steps or sessions, remembering prior interactions, and maintaining workflow continuity over time. This makes them more capable, but it also creates more exposure. The more an agent remembers and the more systems it touches, the more important runtime visibility becomes.

The Four Core Characteristics of AI Agents

AI agents are complex and conduct a range of tasks, but there are four key characteristics of an AI agent.

Autonomy

An AI agent can make decisions about how to pursue a task without requiring a human to script every step. That doesn’t mean it operates with unlimited freedom; it means it has decision-making latitude within a defined scope. This is one of the biggest differences between AI agents and traditional software automation.

Tool Use and Execution

Agents don’t just answer questions from their own model context. They can call APIs, access files, query databases, interact with business systems, and complete actions in other applications. Tool use and workflow execution are central to what makes an agent an agent.

Memory and State Persistence

Many agents carry context across steps or sessions, which helps them act consistently over time. That persistence is useful for productivity, but it also creates governance and security challenges. Persistent memory, accumulated context, and integrated workflows introduce risks that basic prompt-layer controls cannot see.

Goal-Oriented, Proactive Behavior

Agents are designed to move a task toward a defined outcome. Instead of waiting passively for every instruction, they plan, sequence, and act toward completion. Questions about what AI agents are and how they work are really questions about controlled autonomy, not just language generation.

What Does an AI Agent Actually Do?

The answer depends on its scope, its tools, and the systems it can reach. Some agents are designed primarily to observe and summarize, gathering inputs from multiple sources, comparing them, and returning structured findings. Others are built to execute, updating records, moving data, triggering actions, or coordinating tasks across applications. More advanced agents do both.

• A sales agent might read CRM data, identify account changes, and draft outreach based on context.
• A support agent might review customer history, classify a request, and route it with the right supporting details.
• A coding agent might inspect repository context, generate or revise code, and assist with software changes.
• A security agent might summarize alerts, recommend next steps, and prepare incident documentation.

The important point is that capability isn’t just about generating language. It’s about observing the environment, deciding what matters, and acting with access to tools and systems. As soon as a system can reason through tasks, use tools, maintain state, and operate toward a goal, it moves beyond simple assistance and into more autonomous behavior.

Common AI Agent Use Cases & Risks

AI agents are most useful where work is fragmented across tools and where human teams spend too much time stitching together context manually. That same usefulness makes them attractive to non-technical teams. And low-code platforms are making it easier for departments to deploy agents independently. That lowers the barrier to adoption, but it also accelerates shadow AI.

Unsupervised automation can lead to agents making decisions, chaining prompts, and accessing information without a human in the loop. Security teams may have little visibility into what was deployed, what data it touches, or what it can do.

This is why agentic AI is not just a productivity story. It is also a governance story.

Knowledge Retrieval → Risk: RAG Poisoning

Knowledge retrieval agents summarize documents, answer policy questions, and surface project information from platforms like SharePoint, Confluence, or Google Drive. The productivity gain is clear, but so is the attack surface.

OWASP’s LLM Top 10 (2025) classifies this threat under LLM08: Vector and Embedding Weaknesses. When an agent retrieves content to answer a question, it treats that content as trusted context. An attacker who can write to, or influence, the knowledge base doesn’t need to compromise the model directly. They just need to poison what the model reads: embedding false instructions in a shared document, inserting fabricated policy content into a vector database, or manipulating retrieval results so malicious content surfaces ahead of legitimate sources.

The risk compounds in enterprise settings where multiple teams contribute to shared repositories. A single poisoned document can affect every future query that retrieves it, without triggering obvious alerts. The output looks authoritative. The damage is silent.

Security Operations → Risk: Prompt Injection

Security agents connected to SIEMs and threat intelligence platforms help analysts summarize incidents, investigate alerts, and recommend next steps by compressing hours of manual triage into minutes. But they also ingest content from sources outside the organization’s control.

OWASP’s LLM01:2025 on Prompt Injection warns that attackers can craft inputs designed to override an agent’s instructions and redirect its behavior. In a security operations context, that content can enter the pipeline through logs, external threat feeds, or incident data, which are sources the agent is expected to trust. If that content contains malicious instructions, the agent may generate incorrect summaries, surface misleading recommendations, or suppress evidence of real attacker activity. An analyst acting on a poisoned investigation summary during an active incident may miss the actual threat entirely.

The stakes here are higher than in most use cases. Misdirecting a security agent doesn’t just produce a wrong answer—it can conceal an attack in progress.

Customer Support → Risk: Data Leakage via Prompt Injection

Customer-facing support agents draw on account data, order history, and interaction context to answer questions and resolve issues. They reduce handling time and free up human agents for complex cases, but they also process live, sensitive customer data in real time.

OWASP’s LLM01:2025 warns that prompt injection attacks can cause an agent to reveal hidden instructions, bypass workflow guardrails, or take actions outside its intended scope. In a support context, that attack surface includes every message a customer sends. A manipulated agent may expose another customer’s account information, reveal internal instructions it was told to keep confidential, or execute unauthorized actions like issuing refunds or escalating cases. Because these interactions happen at scale without human review, a successful injection can affect many customers before it’s detected.

Regulated Workflows → Risk: Compliance and Accountability

Agents operating in healthcare, finance, legal, and HR workflows may process medical records, financial histories, employee data, contracts, and other regulated material. They can accelerate decision-making and reduce manual overhead, but they also introduce governance obligations that go beyond getting the answer right.

NIST’s AI Risk Management Framework emphasizes that trustworthy AI requires accountability, transparency, and structured risk management. In regulated environments, organizations must demonstrate that AI-assisted decisions were made lawfully, that sensitive data was handled appropriately, and that meaningful human oversight was in place. The question isn’t only whether the agent produced an accurate output, but also whether the organization can show regulators, auditors, or a court that it governed the agent responsibly throughout its operation.

Development Environments → Risk: Source Code and Secret Exposure

Coding agents and AI-assisted development tools like GitHub Copilot review repositories, answer questions about codebases, and generate or suggest code using project context. They work close to some of an organization’s most valuable assets, and they have the access to match.

SecurityWeek reported on a vulnerability in GitHub Copilot Chat that allowed a researcher to leak AWS keys and zero-day bugs from private repositories by combining remote prompt injection with a bypass of GitHub’s Content Security Policy. The attack required no malware, left no obvious trace, and routed exfiltrated data through GitHub’s own trusted infrastructure, invisible to standard network defenses.

The broader lesson: a coding copilot is a privileged actor inside the development environment. When that access can be redirected through prompt injection or malicious repository content, the copilot becomes a data exfiltration path.

Why Securing AI Agents Is Critical

If your organization is moving from generative AI experimentation to operational agent deployment, now is the time to treat agents like enterprise infrastructure, with visibility, policy enforcement, monitoring, and governance across the full agent lifecycle.

Risk is no longer limited to what an AI agent says. It includes what an agent can access, remember, decide, and do. A chatbot that drafts text carries one risk profile. An agent that can access SharePoint, update Salesforce, query an internal database, and launch actions across workflows carries another entirely. Security controls need to see how an agent behaves over time, not just whether a single prompt looks suspicious.

This challenge is growing because AI agents are increasingly built through low-code tools, copilots, and departmental platforms, often without security team involvement. Which agents exist, what data they can access, what tools they can call, what actions they can take, and what happens when they drift from their intended goals are governance questions that become urgent the moment agents move into real business processes.

Zenity is built to help organizations secure and govern AI agents where it matters most: across runtime behavior, tool use, memory, and connected systems. If you are evaluating how to scale AI safely, book a demo to see how Zenity helps teams get ahead of agentic risk.