Knowledge/Reasoning Separation (Recipes)
An agent-architecture principle that separates what an agent knows (a declarative knowledge base) from how it reasons (an explicit, composable procedure defining what to examine, what knowledge to load at each step, which decision procedure to follow, and when analysis is complete) — enabling failures to be diagnosed as either a knowledge gap or a flawed procedure rather than treated as an opaque black box.
Also known as: recipe, recipes, composable recipe, reasoning pipeline, knowledge-gap vs reasoning-flaw diagnosis
How authors use it
Oldest first (extraction date when no publication date is known).
| Author | Their term | Their meaning |
|---|---|---|
| Meta's Recipe for Building Agents as "Organizational Second Brains" Sergio De Simone · 2026-09-09 | Recipe | A composable procedure defining how the agent should analyze a problem: what to examine first, which knowledge to load at each step, which decision procedure to follow, and how to determine the analysis is complete. |
Documents (1)
- Meta's Recipe for Building Agents as "Organizational Second Brains" Sergio De Simone · 2026-09-09 source ↗ Reader 🖥️
To make an AI agent trustworthy as an organizational domain expert rather than just a document-retrieval tool, keep institutional knowledge entirely out of the model's weights and instead encode it in structured, version-controlled, auditable files consulted by an explicit and testable reasoning layer, with human experts as a permanent, recorded correction source — so the agent's expertise compounds reliably through a self-improvement loop instead of eroding or hallucinating over time.
Propositions touching this concept (1)
By publication month, oldest first (extraction month when no publication date is known).
2026-09
- explicitRecipes make it possible to determine whether a failure came from missing knowledge or from a flawed reasoning procedure.
Open gaps (2)
- incompleteThe claimed outcome metrics (days-to-minutes, 'useful almost all the time,' zero regressions) are self-reported by Meta with no sample size, no independent audit, no defined baseline for what 'days' meant before, and no disclosure of failure modes.Meta's Recipe for Building Agents as "Organizational Second Brains" — read next: Read the original Meta engineering blog post for methodology detail, and look for independent replication or a comparable published case study outside Meta.
- uninformedNo technical detail is given on the model(s) used, the cost or latency of the recipe-based reasoning pipeline, or how the 200+ files are authored, reviewed, and kept current at scale — the encoding-format-and-lifecycle question already raised about an earlier Meta agent article remains only partially answered here.Meta's Recipe for Building Agents as "Organizational Second Brains" — read next: Compare against extract-meta-unified-ai-agents-performance-optimization and the original Meta engineering blog post for architecture and lifecycle detail.