AI Agent Design

An agent combines a decision-making component with an environment it can observe and affect. In a language-model agent, prompts and tool descriptions help select actions, while application code executes them and returns observations. Architectures range from a bounded tool loop to a graph with planning, execution and review stages. ReAct illustrates the interleaving of reasoning and environment interaction; other designs separate planning from execution. The defining issue is where decisions are delegated to the model and where deterministic rules control progression. A multi-step workflow does not require every step to be autonomous, and adding more agents is a separate architectural choice.

The practitioner starts with tasks and failure costs, identifies necessary tools and decides which transitions need explicit control. They specify state, completion criteria, error handling and limits on actions or resources. Tool contracts should expose meaningful operations rather than unnecessary implementation detail. A useful design includes a control diagram, permission boundaries and evaluation tasks that exercise recovery as well as success. Simpler deterministic steps can handle known sequences, leaving model decisions for ambiguity that actually benefits from flexible interpretation.

A persuasive reasoning trace is not proof of successful work. Agents may repeat tools, drift from the original goal or accept external text as new instructions. Architecture should be judged by observable task behavior, recoverability and resource use. More elaborate loops can reduce predictability without improving outcomes. A strong design exposes enough state for diagnosis and makes consequential permissions enforceable outside the model, rather than relying on a general request to behave responsibly.