What is agentic AI?

Start with a task, not a label

Agentic AI describes systems in which an AI model can help decide how to progress through a task, often using tools and responding to the results of earlier steps. In its engineering guidance, Anthropic distinguishes a predefined workflow from an agent that can choose its process and tool use. The distinction concerns how work is directed, rather than how a chat interface looks.

For a business, a more useful question than “Do we have an agent?” is “What responsibility have we given this system, and how do we know it has fulfilled it?” That question brings together the task, the available information, permitted actions and the people accountable for the outcome.

An illustrative service enquiry

Consider a team preparing a response to a customer's question. A basic assistant might draft a paragraph from the text supplied. A more connected system could look for relevant information in an approved knowledge base, identify missing details, prepare a response and place it in a review queue.

The business still needs to decide whether the system can access customer records, whether it may send an answer, which person reviews it and what happens when the sources disagree. Calling the system an agent does not answer any of those questions.

Separate capability from permission

A model's ability to propose an action does not mean the application should execute it. The surrounding software defines which tools exist, who can use them, what inputs are accepted and which operations require a separate approval.

Choose a level of flexibility that serves the work

If every case follows the same known steps, a fixed workflow may be easier to understand and evaluate. If the next step depends on what the system discovers, more flexible orchestration may be worth examining. The choice should follow the task's actual variation and the team's ability to assess the result.

For example, preparing a standard document from a completed form differs from investigating why information is missing across several systems. They may share an AI model while needing different application designs.

Make a first project measurable

Write down what a completed task looks like. Gather representative cases and identify the mistakes that would matter. Include examples with missing information, unavailable tools and requests outside the agreed scope. Ask the responsible team to review the result using a consistent standard.

Those observations can support a practical decision: continue with the design, adjust the task boundary, improve the sources or choose a simpler approach. An evaluation is valuable when it changes a decision, not merely when it produces a score.

Take the next step

Our agentic AI consulting helps shape that initial decision. Once the task is clear, AI agent development turns the agreed scope into a system your team can evaluate.

For the next preparation exercise, read Choosing your first AI agent use case.

Further reading

Anthropic: Building effective agents explains the distinction between workflows and agents and provides engineering examples.