Agentic AI: How It Works and When to Use It

An AI agent can look up a customer record, decide that something is missing, search another system and prepare the next action. That makes it a different business proposition from AI that drafts a reply. You are deciding how much freedom software should have to pursue a task.

The useful question is whether choosing the next step dynamically improves the work enough to justify its extra cost, errors and supervision. Different requests do not automatically require an agent. A form, an integration or a fixed workflow with one AI step may handle the variation perfectly well.

We would assess agentic AI by the part of the job that needs judgment about what to investigate next, then separately decide what the system may commit your business to doing.

How an AI agent works

Here, agentic AI means software that uses a language model—the AI that interprets and generates text—to choose steps toward a goal. Terminology varies. A useful distinction comes from Anthropic’s explanation of workflows and agents: a workflow follows paths defined in code; an agent lets a language model direct the sequence of actions and tools. That distinction matters more than the product label.

In a typical agent loop, the model receives a goal, instructions, relevant information and descriptions of available tools. It proposes a tool call, such as retrieving a service record. The surrounding software checks and executes an allowed call, then returns the result. The model uses that result to propose another action, produce an answer or stop. The process needs limits on time, cost and repeated attempts.

A tool result can change the next step. An unavailable record may lead to a different search or an escalation. But a model’s statement that it has completed the task is not proof that the intended business result occurred.

Memory is another source of confusion. An agent can retain a summary or retrieve earlier notes to continue work later. Those notes become information available to the model, as Anthropic describes in its context-engineering guidance. Memory and tool feedback do not automatically retrain the underlying model. A system can retain an incorrect conclusion, too.

One service request, two sensible solutions

Consider a hypothetical company that services commercial equipment. A customer emails to request a visit next week. The message includes an equipment description but no asset number. Before arranging the visit, a coordinator needs to identify the equipment, check the applicable service agreement and confirm the customer’s access window.

For most requests, the work may be predictable. An AI step extracts the site, equipment description and requested dates. Ordinary software retrieves the matching records. Rules identify missing fields and send ambiguous cases to a coordinator. If the existing service-management system already supports this, configuration may be sufficient. An integration may resolve the remaining problem of records sitting in separate systems.

That is a credible outcome even if every email looks different. Language varies; the required sequence may remain stable.

Now consider a more difficult request. The customer describes the equipment by an old internal name. A previous visit mentions a planned replacement. The asset register still shows the original unit. The coordinator must decide whether to inspect the replacement handover, search later correspondence or ask the customer for identification. The answer determines which agreement and service history matter.

A bounded agent could be useful here if this kind of investigation consumes substantial staff effort. It might find the replacement note, retrieve the handover record and discover that the replacement has a different identifier. That discovery changes which agreement it needs to inspect.

In the successful path, it assembles a proposed job record with the equipment identity, relevant agreement, current access requirements and links to the evidence. A coordinator can then review the record and authorize the visit. The agent has chosen how to gather the information; it has not promised a date or decided that disputed work is covered.

The failure path is equally important. It could mistake “replacement planned” for “replacement completed,” attach the wrong agreement and produce a convincing but incorrect proposal. A longer investigation would then have created more work for the reviewer. If the records conflict, the equipment cannot be identified or the investigation reaches its agreed limit, the correct outcome is to stop and hand over the unresolved question.

This example still does not establish that an agent is worthwhile. If these exceptions reduce to a few recurring branches, a fixed workflow may handle them more reliably. The candidate for an agent is the investigation that benefits from selecting different evidence as it unfolds—and that produces a result staff can verify without repeating the entire investigation.

Freedom to investigate is different from permission to commit

A business can give an agent room to search permitted records while keeping authority over bookings, spending, contractual promises and changes to important records tightly restricted. Autonomy does not need to rise equally across every action.

In the service-request example, an investigation agent might have no booking capability at all. It could prepare a proposed job in a review queue. Separate software would require an authorized person’s approval before making the booking. That boundary should be enforced by the application and its permissions, rather than depend on the model remembering an instruction.

Microsoft’s guidance on agentic risk recommends minimum necessary access, deterministic controls for prohibited actions, meaningful approval points, activity records and ways to interrupt autonomous behavior. It also identifies prompt injection: untrusted material, such as a document or incoming message, can contain instructions intended to redirect an agent. Access to a customer email must not make that email an authority to change the agent’s permissions.

Review also needs a usable design. A coordinator should see the proposed action, the records supporting it, unresolved conflicts and any relevant information that changed during the investigation. A polished summary with an “Approve” button can hide the very uncertainty the person is supposed to assess.

The reviewer needs time and authority to reject or correct the proposal. If every approval requires reopening all the original records, include that effort in the business case. Human oversight has an operating cost, and adding a person at the end does not automatically make a weak process dependable.

What would make the pilot worth approving?

Start with the work that remains after the simpler improvements. These are different ways to solve a problem, not stages that every business must progress through.

These choices can overlap. An existing product may already offer an agent. Check whether its supported features or supplier configuration meet the investigation, permissions and review requirements before commissioning custom development.

An agent pilot becomes more persuasive when the unresolved work involves choosing which evidence to pursue, when that choice changes as information arrives, and when the resulting recommendation can be checked at reasonable cost. Reliable access to the relevant records is a prerequisite. An agent cannot compensate for records it is not allowed to retrieve or systems that expose no usable access.

Compare the candidate against the best practical simpler approach. For the service company, that might be a configured intake workflow with an AI extraction step and a human exception queue. Testing only against today’s entirely manual process could credit the agent for improvements that ordinary automation would also deliver.

Use representative requests, including incomplete, contradictory and changing information. Examine how often staff accept the prepared work, how long review takes, what corrections are required and whether the system stops appropriately. Also count duplicated actions, unnecessary searches and the effort of investigating failures. A faster first draft may still produce a slower completed job.

Then assess the expected workload. A useful improvement on each request may still be too small or too infrequent to justify implementation and integration costs. Count recurring software and model charges, maintenance, staff review and recovery work. Compare those costs with the additional benefit over the simpler solution; time spent checking the agent’s work belongs in that calculation.

Agree in advance what would justify continuing. The relevant evidence is a better completed workflow at an acceptable total cost and risk. If the agent produces little additional benefit over the simpler baseline, keeping the simpler solution is a successful decision.

Regulation follows the use, data and decisions

Legal obligations depend on what the system does, where it operates and the roles of the organizations involved. The European Commission’s AI Act guidance explains that agents are covered by the existing framework; “agent” is not a separate legal category. Assess the intended use and applicable requirements before deciding which controls are sufficient.

The Act applies in stages. Transparency rules generally began applying on 2 August 2026, with transitional provisions for some systems. Under the amended application timetable, the relevant high-risk requirements apply from 2 December 2027 for Annex III systems and 2 August 2028 for high-risk systems associated with regulated products under Annex I. Those requirements include Article 14’s proportionate human oversight, with the ability to disregard, override or stop the system where appropriate. This is not a universal approval arrangement for every agent.

Personal-data processing raises separate questions. Under the GDPR, processing needs an appropriate lawful basis; consent is one possibility, not the only one. Access, retention and disclosure need attention even when the agent only prepares recommendations.

The GDPR also places restrictions on solely automated decisions producing legal or similarly significant effects, subject to qualified exceptions and safeguards. A routine administrative suggestion and a consequential decision about a person therefore need different assessments. Meaningful human involvement requires a person who can assess and change the decision; a review button alone does not establish it. The proposed workflow needs legal and operational assessment in its actual context; an “agent-ready” product label cannot settle those questions.

Start with the unresolved part of the work

At LegioSoft, the useful starting point is one workflow and the difficulty left after examining existing software. Where custom development is justified, we could bring the relevant records into one review process and add an agent to investigate cases that need further evidence. Staff would receive a proposed action with the records supporting it. What the system could access and do would depend on the available connections, business rules and who is responsible for each decision.

That gives an agent a specific job to prove: reduce the effort of preparing a sound decision without creating a larger checking burden. If you are considering agentic AI, talk to us about one workflow. We can help assess whether your existing tools can resolve the problem and, if they cannot, whether conventional automation or an agent pilot is worth pursuing.