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AI agents and automation systems

An agent combines model decisions with tools and task state. Its usefulness depends on the actions it can take, what it knows about its progress and how it detects completion. Greater autonomy requires a more precise definition of the work.

Distinguish capability from autonomy

A model capable of calling functions is not a complete operational system on its own. It needs an executor that validates arguments, enforces permissions and records results. Compare the actual scope of tools, data access and stopping conditions. An API accepting an action does not prove that the final result is correct.

A concrete example

To organize documents, an agent might classify files, propose folders and detect duplicates. Separating proposals from file moves lets you review permissions and recover state after a failure. Measure success against the resulting files and their integrity as well as the model’s explanation.

What to check

Before enabling external actions, define what is allowed, what needs confirmation and what must stop the workflow. Set a task budget, an attempt limit and a way to reconcile uncertain operations without executing them again.

Agents and automation

Systems that combine models, tools, memory and control rules.

PATTERN

Agent with tools

Select actions, consult services and verify the result.

Tools PATTERN

Monitored flow

Reserve sensitive decisions for explicit human review.

Control PATTERN

Recovery with sources

Connect answers with documents and traceable references.

RAG
ANALYSIS

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Keep exploring

Models Tools Text and reasoning Programming Image, video, voice and audio
SourcesNIST AI RMFwww.nist.gov