AI Agents

Some work involves more than producing an answer to a question; it consists of connected steps such as gathering information, evaluating it, preparing a decision and taking action across different systems. When people coordinate these steps manually across applications, problems with time, tracking and consistency can arise. AI agents can help execute multi-step tasks whose boundaries are clearly defined.

AI models can now connect to enterprise data, software and different tools, rather than only generate text. This development is turning AI from an assistant into an operational component capable of performing defined work. Its broader ability to act makes process design, authorization, monitoring and human oversight even more important.

AI agents can reduce repetitive coordination work, accelerate access to information and help defined tasks run more consistently. They can redirect employees’ time from data gathering and cross-system follow-up toward higher-value work such as evaluation, exception management and decision-making. The value created should be assessed through indicators such as completion time, human intervention, errors, service levels and transaction quality.

A standard AI assistant generally produces a response to a user’s question or instruction. An AI agent can break a goal into subtasks, use tools, track task status and take action within defined boundaries. Being an agent does not mean having unlimited autonomy; access, decision and execution permissions must be constrained according to the use case.

Select the first use case from a limited process with clearly defined goals, inputs, outputs and success criteria. Define from the outset which data and tools the agent can access, which actions it can take, where human approval is required and what conditions will stop it. Do not grant broad permissions before results are validated, and ensure every action is traceable.