AI Planning Frameworks

Quick Guide: Default to the simplest tier that satisfies the requirement — most features are one well-built model call. Pin the model id, define the output contract with a repair-vs-reject policy, budget in tokens and money rather than adjectives, enumerate the failure modes, and make quality measurable with an eval plan before implementation starts. Apply a framework only when the spec touches its artifact class — a feature with no retrieval needs no retrieval section.

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CRITICAL: Before Specifying AI Features

All specifications must be grounded in the codebase's real model clients, prompt modules, schemas, and eval fixtures — reference specific files with line numbers

(You MUST justify the approach against the simpler tier — a fixed code-orchestrated chain beats an agentic loop whenever the step sequence is known)

(You MUST pin an explicit model id in configuration with a named fallback — never a floating alias, never inline in code)

(You MUST define the output contract completely: mechanism, schema, validation boundary, and a repair-vs-reject policy)

(You MUST state budgets as numbers — tokens per call, calls per request, cost per request, p95 latency — never as adjectives)