Context Store

A deterministic, versioned record of design intent, behavior, and architectural conformance — produced by running spec-anchored SDD, TDD, and architectural fitness functions together — that humans and AI agents both query across a system's lifecycle.

Also known as: context store, queryable context store, deterministic context store

Part of: Knowledge Base

How authors use it

Oldest first (extraction date when no publication date is known).

Documents (3)

• The Work Behind Delegation: A Framework for Supervising AI Coding Agents Yeon Su Park; Nadia Arvi; Hae Ri Lee; Sehoon Lim; Qianou Ma; Juho Kim · 2026-09-22 Reader 🖥️
  Developer supervision of AI coding agents is not a single activity performed at fixed checkpoints but a connected process organized into seven distinct, loop-connected stages (Plan, Monitor, Wait, Review, Teach, Manual Fix, Update Assets) -- a reconfiguration of Sheridan's classical five-stage human-supervisory-control framework made necessary by agents' greater autonomy -- and developers manage the resulting cross-stage demands (alignment, quality, understanding) by concentrating effort in planning, distributing supervisory work to other agents and automated hooks, and externalizing recurring corrections into reusable assets.
• Human judgment doesn't leave the software factory. It relocates. Addy Osmani · 2026-08-21 source ↗ Reader 🖥️
  As software development moves from hand-coding to operating repeatable, event-driven 'software factories' of coding agents, human judgment does not leave the loop -- it relocates upstream to intent, system design, and the quality bar, and downstream to verification design, risk, and ownership of what ships -- so the best factories are defined by how intelligently they place human attention, not by how completely they remove it.
• Comprehension at AI Speed: Building a Context Store for Evolutionary Architecture Stella Berhe, Stephan Bragner, Vikram Maran, Anand Jayaraman · 2026-07-14 source ↗ Reader 🖥️
  Because AI has decoupled writing code from understanding it, engineering organizations must run spec-anchored specification-driven development, test-driven development, and architectural fitness functions together as one verification system so that their combined artifacts form a versioned, queryable "context store" — making comprehension a lifecycle property of evolutionary architecture that both human reviewers and AI agents can draw on, rather than a pre-merge concern that AI-generated speed otherwise outruns.

Propositions touching this concept (3)

By publication month, oldest first (extraction month when no publication date is known).

2026-07

• explicitdevelopedA context store — a deterministic, versioned record of intent, behavior, and architectural conformance, whose source of truth is the repository's own artifacts rather than an LLM's memory of them — is what organizations actually need to close the context gap.
  Comprehension at AI Speed: Building a Context Store for Evolutionary Architecture · p5
• explicitRunning spec-anchored SDD, TDD, and architectural fitness functions together, on one cadence, as a single verification system produces the context store as an emergent byproduct; none of the three disciplines is individually new, but their deliberate integration is.
  Comprehension at AI Speed: Building a Context Store for Evolutionary Architecture · p6
• explicitThe context store is a lifecycle artifact rather than a pre-merge one: before deployment it briefs AI agents and grounds reviewer interrogation, and after deployment it supports debugging, architecture planning, and system-integration decisions.
  Comprehension at AI Speed: Building a Context Store for Evolutionary Architecture · p9

Open gaps (3)

• incompleteThe Amazon outage is presented as an outcome of the exact context gap this article diagnoses, but the article's own cited source (CNBC) attributes the outage to "changes merged without proper review" — a review-gate failure — not to a documented absence of persisted intent, behavior, or conformance records specifically. The causal attribution to the context gap, versus a more generic review-diligence lapse, is asserted rather than demonstrated from the available reporting.
  Comprehension at AI Speed: Building a Context Store for Evolutionary Architecture — read next: Follow further Amazon incident reporting or post-mortems, if published, to see whether investigators identified a missing intent/behavior/conformance record specifically, or a simpler story of a skipped approval step that a context store would not by itself have prevented.
• incompleteOsmani notes that 'code often preserves a decision that was made, but not why the decision was made' and suggests agents should store trajectory/lessons information, but this stays a casual aside -- no format, storage location, retrieval mechanism, or team-sharing design is specified, even though it's exactly what would resolve the comprehension-debt problem his own 'feature I had to relearn' story illustrates as a serious, recurring cost.
  Human judgment doesn't leave the software factory. It relocates. — read next: A concrete design for a durable, queryable record of agent decision rationale that survives context compaction and is shared across a team, rather than left to one operator's memory or an ad hoc local note.
• incompleteThe workflow-diagramming method relied on retrospective reconstruction after a single ~40-minute task, and participants were introduced to Sheridan's five-stage framework before drawing their own diagrams -- a sequencing the authors themselves flag (§9) as a possible anchoring effect on how participants segmented their own supervision, even though they were explicitly told not to follow it.
  The Work Behind Delegation: A Framework for Supervising AI Coding Agents — read next: A within- or between-subjects comparison of workflow diagrams elicited with versus without prior exposure to Sheridan's framework, to measure the anchoring effect directly rather than only naming it as a limitation.