Planner, Generator, Evaluator: Agentic AI Architecture

Agentic AI architecture combines reasoning models, memory, tools, orchestration, and evaluation to complete multistep goals, with the evaluator presented as a critical production component often omitted from common diagrams. The proposed planner-generator-evaluator separation has planners define machine-checkable acceptance criteria before implementation, generators create code or perform actions without judging their own work, and independent evaluators test artifacts in a separate context and return criterion-level verdicts supported by evidence. This division is intended to mitigate self-attribution bias, in which models may assess their own prior outputs more leniently, and to prevent criteria drift, context leakage, and unsupported success claims. For user-facing software, the discussion argues that browser-grounded checks of rendered interfaces, URLs, network behavior, logs, and screenshots provide stronger validation than source-level tests alone, citing Kane CLI as an example of a tool that can produce reproducible evidence packs. Although multi-agent evaluation increases token use, latency, and system complexity, it is positioned as most valuable for high-risk workflows such as payments, authentication, migrations, and customer-facing features, while deterministic external checks may be preferable to additional model-based reasoning where possible.

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