AI agents
An AI agent is a system that selects and carries out actions in pursuit of a task or objective. It may call tools, interact with software, or operate in a simulated or physical environment. The term covers many designs and does not by itself imply independent judgment, reliability, or unrestricted autonomy.
How it works
An agent typically observes relevant information, chooses an action, executes it, and uses feedback to decide what to do next. Some workflows follow tightly defined steps; others permit more flexible planning. State tracking, tool interfaces, permissions, and error recovery are part of the practical system.
Evaluation should inspect outcomes and constraints rather than rely only on fluent explanations. A system that describes a successful action has not necessarily performed it.
Why it matters for licensing
Agent-oriented data can include task instructions, observations, action sequences, and verified outcomes. Business records may support some of these elements, but missing context or permissions can limit suitability. The type of agent and intended task determine what data is useful.
Example
Fictional example: An agent reviews an inventory report and prepares a proposed replenishment order for human approval. Its permissions allow reading inventory and saving a draft but not sending the order.
Limitations and misconceptions
Agents can misunderstand instructions, use stale information, or act incorrectly after a tool failure. A benchmark result does not establish safe operation in every setting. Human approval and restricted permissions may be necessary for consequential actions.
Questions to ask
- What actions can the system take, and which require approval?
- How are success, failure, and unintended side effects observed?
- What context and recovery behavior does the data capture?