AI Agents

AI agents are software systems that pursue a goal by reasoning about which steps to take, using tools to act on data and applications, and adjusting as conditions change. Where a chatbot answers, an agent acts, so its results are only as reliable as the data and context it works from.

Building an AI agent now takes an afternoon. Keeping one accurate takes more. Gartner predicts that over 40% of agentic AI projects will be canceled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls.

This entry explains what AI agents are and how they work, then covers what most definitions skip: why some agents hold up in production while others decay after launch. The difference usually comes down to the data, context, and governance underneath the agent.

What are AI agents?

An AI agent takes a goal, decides what to do, and executes. Most agents combine four parts: a language model that reasons about the task, tools the agent can call (database queries, APIs, ticketing systems), memory of what it has already done, and rules about what it is allowed to do. Large language models (LLMs) store and recall knowledge. An agent has to do both: know things and act on them.

Agents also differ from robotic process automation (RPA). RPA follows fixed, deterministic rules and breaks when an input changes shape. An agent reasons probabilistically, so it can handle ambiguity, at the cost of producing different paths and outputs from similar inputs. That flexibility is the reason agents need stronger controls than scripted automation.

Three properties set AI agents apart:

• Goal orientation. An agent pursues an outcome, such as resolving a ticket or documenting a set of tables, not a single scripted task.

• Adaptability. It reassesses its approach as new data, tools, or conditions appear.

• Reusability. Once built, an agent can be shared, cloned, and customized by other teams.

An IT support agent is a familiar example. Such an agent understands a request in plain language, troubleshoots the problem, resets a password, and opens a ticket for a human when it cannot resolve the issue. A data example: an agent that scans a catalog for undocumented tables, drafts descriptions, and routes them to a steward for approval.

How do enterprise AI agents work?

Unlike standard language models that generate a response in a single pass, an AI agent operates in an iterative feedback loop: assessing its environment, choosing an action, evaluating the outcome, and adapting until it reaches a goal. In an enterprise setting, that architecture relies on four foundational components:

Reasoning and planning

The agent receives a goal, uses its model to break the goal into steps, and picks the next step based on what it has learned so far. After each action, it checks the result and decides whether to continue, retry, or escalate.

Tools and actions

Tools are how an agent changes things outside the model: running a SQL query, calling a REST API, sending a message. Open standards such as the Model Context Protocol (MCP) give agents a common way to discover and call tools, so teams do not rebuild each integration. See Alation connectors and SDKs for how Alation exposes its context this way.

Context and memory

An agent that reads raw schemas has to guess what a column or metric means. An agent that reads certified metadata, governed ontologies, and packaged data products has the context it needs to choose the definitions accurate for the business problem at hand. In the enterprise, this is the most common reason two agents answer the same question differently.

Guardrails and oversight

Guardrails limit what an agent may read, change, or approve. They include access controls, approval steps for high-risk actions, and logs that show what the agent did and why. Without them, an agent's mistakes scale as fast as its successes.

AI agents vs. chatbots, RPA, and agentic AI

While the market frequently conflates these technologies, chatbots, robotic process automation (RPA), and AI agents have fundamentally different architectures, autonomy levels, and risk profiles. Choosing the right tool depends on whether the task demands information retrieval, deterministic execution, or probabilistic reasoning across external systems.

The choice of, "which?" depends on the question you are asking. Use a chatbot when the question is what the organization knows, RPA when it is how to repeat a process exactly, and an agent when it is how to get something done, particularly when the path varies.

Agentic AI is the broader term for systems that act with some autonomy toward a goal, often by orchestrating several agents; an AI agent is one such system. Gartner uses the term "agent washing" for vendors that rebrand assistants, RPA, and chatbots as agents without substantial agentic capability. Checking whether a product plans, uses tools, and acts is the quickest way to tell the difference.

Levels of autonomy in enterprise AI agents

Autonomy is a spectrum, and most enterprise agents sit in the middle of it.

• Assistive. The agent drafts or recommends, and a person approves every action.

• Workflow-bound. Agents run as steps in a defined flow, on demand or on a schedule, with access controls and accuracy checks at each handoff. (Alation agentic automation works this way, which suits processes that must run exactly as designed.)

• Autonomous within guardrails. The agent chooses its own steps toward a goal but cannot exceed the access and approval limits set for it.

Scripted automation remains a reasonable choice when a process never varies. Agents earn their cost where the path changes from case to case. Gartner's guidance is to use agents when decisions are involved, automation for routine workflows, and assistants for simple retrieval.

Types of AI agents

Classical AI research groups agents by how they decide: simple reflex agents react to the current input, model-based agents track the state of their environment, goal-based agents plan toward an outcome, utility-based agents weigh trade-offs between outcomes, and learning agents improve from feedback.

While most enterprise agents built on LLMs operate as goal-based systems, production-grade architectures incorporate continuous learning loops. By systematically capturing human corrections and operational feedback to refine the agent's context at the source, enterprise deployments have demonstrated measurable gains in output accuracy and long-term reliability.

Use cases of AI agents in the enterprise

In the enterprise, AI agents deliver the highest return where workflows are too complex for rigid scripting but too high-volume for manual human execution alone. By grounding probabilistic reasoning in governed data and enterprise context, organizations deploy agents to automate data operations, scale self-service analytics, and establish continuous oversight across the following key areas:

Documenting and curating data

Agents draft descriptions, surface the assets analysts already rely on, and close gaps in documentation and steward assignments, so curation keeps pace with the data estate instead of trailing it.

Monitoring data quality

Agents apply and scale quality rules across assets. With data quality scores surfaced at the point of decision, a deprecated source is less likely to end up in a board deck.

Answering business questions in plain language

Agents translate a question into a governed query and return an answer with the definition it used. Conversational analytics built this way lets people ask without writing SQL or waiting for an analyst. Euromonitor uses Alation to give customers conversational access to more than 50 years of research, as described in its case study.

Governing critical data

Agents identify and map critical data elements and monitor their quality continuously. Brambles uses agents to link critical data elements across hundreds of thousands of objects, saving hundreds of hours, according to its story. See Critical Data Manager and agentic data governance.

Governing AI itself

As agents multiply across platforms, someone has to know which ones exist, what data they use, and which regulations apply. AI governance registers every model, agent, and tool, and maps each to the rules that apply to it.

Tracing issues in pipelines

Agents that read lineage can trace a broken metric to its source and send the alert with the context needed to fix it. See pipelines and observability.

The same pattern appears across industries. In financial services, agents monitor data access for regulatory compliance. In healthcare, they apply access policies based on data sensitivity. In manufacturing, they analyze metadata on suppliers, inventory, and logistics.

Business outcomes of AI agents

When AI agents are grounded in governed metadata and certified context, their return extends well beyond simple time savings to organizational consistency and enterprise agility. Successful deployments deliver measurable impact across five core operational areas:

• Less manual stewardship. Agents handle documentation and metadata gaps at the pace the data changes, so stewards review and approve instead of writing from scratch.

• Faster answers. People ask in plain language and receive an answer grounded in certified data, with the definition named.

• Consistent definitions. When two teams define churn differently, a grounded agent applies the definition that governs the question, so people and agents get the same answer.

• Audit readiness. A registry of agents, mapped to regulations with evidence attached, replaces evidence assembled by hand before an audit.

• Less rework. Evaluation sets catch accuracy regressions before users do.

Capturing these outcomes requires more than building a clever prototype. The very autonomy that drives enterprise value also magnifies operational vulnerabilities when agents are released into uncontrolled data environments, explaining why so many initiatives stall after go-live.

Why AI agents fail in production

Demonstrating an agent in a controlled sandbox is deceptively straightforward; maintaining accuracy in a dynamic enterprise data estate is where most initiatives collapse. Production failures rarely trace back to model reasoning limits; rather, they stem from brittle context, unchecked access, and fragmented governance:

• Context drift. Schemas, definitions, and owners change after launch. An agent built on a snapshot of last quarter's context degrades quietly.

• Conflicting definitions. Without governed business definitions, an agent picks whichever version of a metric it finds first.

• Agent sprawl. Teams build agents on different platforms with no shared inventory, so no one can say which agents touch which data.

• Over-broad access. An agent connected to many tools widens the surface for data exposure unless access follows the person or process it acts for.

• Unclear value. Many use cases labeled agentic do not need an agent. Gartner links project cancellations to cost and unclear value as much as to technical limits.

Preventing these points of failure requires treating agent development as an engineered software and data lifecycle rather than an isolated prompt-engineering project. Bridging the gap between a fragile pilot and a resilient enterprise asset demands a structured, governance-first rollout.

How to deploy AI agents

Moving an AI agent from concept to production requires balancing speed with strict architectural guardrails. To maximize business value while mitigating operational and regulatory risk, data leaders should follow a seven-step deployment framework:

1. Choose a process where the path varies and the value is measurable. Use automation for fixed workflows.

2. Ground the agent in certified metadata, governed definitions, and data products, not raw schemas.

3. Build evaluation sets from real query history and set an accuracy threshold the agent must pass before production. Alation recommends teams typically gate above 90%.

4. Set autonomy limits and access controls, and decide which actions need human approval.

5. Use open interfaces so agents can reach people in the tools they already use, and so agents built elsewhere can draw on the same context.

6. Monitor accuracy after launch, and route corrections to the source of the problem so every agent that reads that context improves.

7. Register the agent in an inventory with its data dependencies and applicable regulations.

On interoperability, MCP connects an agent to tools and context, while the Agent2Agent (A2A) protocol connects agents to each other. Both are now hosted by the Linux Foundation, which gives teams a vendor-neutral basis for multi-agent designs. Alation lists MCP among the open standards it supports.

How Alation helps with AI agents

Agents grounded in certified context. Alation Agents read certified metadata, data products, and ontologies instead of raw schemas, so they use agreed business definitions. When a revenue or churn number comes back, the answer names the definition it used. That is possible because the context comes from a governed system of record, not from whatever an agent finds in a schema.

Accuracy that is proven and then maintained. Teams build evaluation sets from their own query history and gate agents against an accuracy target before production. After launch, Alation monitors accuracy as the catalog changes. When it drops, Alation traces which piece of context caused the error and proposes a fix at the source. A steward approves it once, and every agent that reads that context is corrected.

Build, bring, or connect, and reach people where they work. Native curation, lineage, and chat agents ship ready to run, and teams can clone them to change the prompt, tools, or model, on GPT, Claude, Gemini, or a model they host. Agents built elsewhere can connect and draw on Alation for context and governance. Agents are exposed over MCP and REST, so they appear in Slack, Teams, Copilot, Claude, and ChatGPT, and they can be chained into flows that run on demand or on a schedule. See Agent Studio and connectors and SDKs.

Governance that covers the agents themselves. Alation AI Governance registers every model, agent, and AI tool across platforms, maps each to applicable regulations, and routes approvals through regulation-aware workflows. Because it extends data governance, each registered agent links back to the data it depends on. Agentic data governance applies the same policies whether a person or an agent is asking.

The same answer for people and agents. Governed AI/BI applies one governed definition to each metric, whether the question comes from an analyst, a dashboard, or an agent. Alation ingests semantic models from existing BI tools rather than asking teams to rewrite them.

For a CDO evaluating agents, the question to ask of any platform is what happens in month six: who notices when accuracy drops, and what it takes to fix it everywhere at once. Alation built AIOS to answer that question, and more than 500 enterprises rely on Alation and AIOS in production.

Frequently asked questions

What is an AI agent in simple terms?

An AI agent is software that takes a goal, decides which steps to take, and uses tools to carry them out, such as querying a database or filing a ticket. Unlike a chatbot, which responds to a question, an agent acts, and it adjusts its approach as new information arrives.

What is the difference between an AI agent and agentic AI?

An AI agent is a single system that pursues a goal using an AI model and tools. Agentic AI is the broader approach of building systems that act with some autonomy, often by orchestrating several agents. In practice, agentic AI describes the capability, and an AI agent is one way to deliver it.

How is an AI agent different from a chatbot?

A chatbot generates responses to questions, usually without changing anything in other systems. An AI agent can also act: it can run queries, update records, trigger workflows, and decide the next step based on the result. Many products marketed as agents are still chatbots or scripted automation, which analysts call agent washing.

How do AI agents work?

An AI agent receives a goal, uses a language model to plan steps, calls tools to act on data and applications, and checks the result before deciding what to do next. Memory carries context between steps, and guardrails such as access controls and approval rules limit what the agent is allowed to do.

What data do AI agents need to be accurate?

AI agents need governed, well-documented data: certified metadata, agreed business definitions, and clear ownership. Without them, an agent guesses what columns and metrics mean and can return confident but wrong answers. Evaluation sets built from real queries help teams measure accuracy before production and catch drift afterward.

Next steps: Learn more about self-service analytics

Dive deeper into AI agents and how they drive business outcomes by checking out the following resources: