Executive Actionable Research Brief

2026-03-02 · run=20260302T021132Z · status=PASS · inserted=1 · candidates=75

Core read: Treat AI software delivery as an orchestrated factory with explicit multi-lane roles, structured design debate, autonomous implementation, and synthesis-based review—rather than single-model coding sessions.

High-Conviction Findings

Treat AI software delivery as an orchestrated factory with explicit multi-lane roles, structured design debate, autonomous implementation, and synthesis-based review—rather than single-model coding sessions.

art-2026-03-02-001 · design-lane · confidence=medium · evidence=medium

Structured multi-round debate protocol before coding, with explicit challenge/concession behavior to force tradeoff surfacing.

source · research/kb/queue/queue-art-2026-03-02-001-agentic-software-factory-case-study.md

Enterprise agent systems should be designed as composable pattern stacks rather than single-agent chat interfaces; durable production value depends on combining tool use, reflection, planning, multi-agent orchestration, and adaptive reasoning.

art-2026-03-02-002 · design-lane · confidence=medium · evidence=medium

Five-pattern taxonomy: tool use, reflection, planning, multi-agent orchestration, and ReAct loops.

source · research/kb/queue/queue-art-2026-03-02-002-azure-agent-factory-patterns.md

Ralph is an autonomous coding loop runner that repeatedly executes fresh AI coding sessions against a PRD task list (passes: false -> true) until completion, with memory persisted outside the active context window.

art-2026-03-01-030 · agentic-software-factory · confidence=high · evidence=high

Fresh-instance execution discipline: each iteration starts with clean context, reducing drift from long conversational histories.

source · research/kb/queue/queue-art-2026-03-01-030-ralph.md

Coverage + Actions

processed=15 · qualified=5 · decisions={"retain":5} · confidence={"medium":3,"high":2}

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