AI Agents

Early AI assistants answered questions. You asked, they responded, and anything that needed to happen next was up to you. AI agents go further. Given a goal, such as resolving a support ticket, preparing a report or updating a set of records, an agent can break the goal into steps, look up information, call tools and other systems, check its results and continue until the task is done or it needs help. This shift from answering to acting is why agents have become a central topic in enterprise technology.

Agents are built around a language model that does the reasoning, combined with access to tools, such as APIs, databases, search and other software, and some form of memory or context about the task and the organization. Standards such as the Model Context Protocol make it easier to connect agents to tools, and protocols such as Agent2Agent help agents work with each other. Agents range from narrow assistants that perform one kind of task to more general systems that handle varied work under supervision. For executives, operations leaders, technology teams, risk and governance teams and anyone redesigning work, AI agents raise practical questions about value, control, cost and accountability.

Definition of AI Agents

AI agents are software systems in which an AI model, typically a large language model, interprets a goal, plans and carries out a sequence of steps, uses tools, data and other systems to gather information and take actions, evaluates its progress and adapts, operating with a defined level of autonomy within permissions and guardrails set by people.

They are the building blocks of agentic AI, often connect to systems through the Model Context Protocol (MCP) and are governed through agentic AI governance.

Components of an AI Agent

Model. The AI model that interprets instructions, reasons about the task and decides what to do next.

Instructions and goals. The task, role, constraints and policies the agent is given.

Tools. Functions the agent can call, such as searching, reading documents, querying databases, sending messages or updating records.

Context and memory. Information about the task, the user and the organization, along with a record of what the agent has already done.

Guardrails and permissions. Limits on what the agent can access and do, including when it must ask a person for approval.

Levels of Agent Autonomy

Assistive. The agent suggests actions or drafts, and a person decides and acts.

Supervised. The agent acts on routine steps, and a person approves important or risky actions.

Delegated. The agent completes whole tasks within defined limits, and people review outcomes and exceptions.

The right level depends on the risk of the task, the reliability of the agent and the cost of errors.

Importance of AI Agents in Enterprise Operations

Agents can take on real work. Tasks such as triaging requests, preparing documents, reconciling data and updating systems can be handled end to end.

They extend capacity. Teams can handle more work without proportional increases in headcount, or redirect people to higher-value activities. Work that piles up in queues, such as reviews and requests, can be cleared faster.

They also change how work is designed. Processes can be rebuilt around agents doing much of the work, with people setting direction and handling judgment calls.

AI Agents and Financial Management

Agents shift some costs from labor to model usage, compute and software, which need to be tracked and forecast.

Costs per task can vary widely depending on the model used and the number of steps, so measures such as cost per task are important.

Vendors are experimenting with new pricing for agents, such as per task or per outcome, which affects budgeting and contracts.

Value should be measured through outcomes, such as cycle time, quality and capacity freed, rather than activity alone. An agent that completes many tasks poorly may cost more than it saves.

Common Challenges

Agents make mistakes, such as misunderstanding instructions or acting on incorrect information, which can be costly if not caught. Errors are more likely on unusual cases the agent was not designed or tested for.

Agents with broad permissions create security risks, including manipulation through prompt injection. An agent that reads external content can be tricked into taking actions its owners never intended.

Organizations lose track of which agents exist, what they can access and who is accountable for them. Agents created by individual teams can multiply quickly without central visibility.

Agents work poorly when the data, knowledge and context they rely on are incomplete or out of date. Improving that context often improves agent performance more than changing the model.