SR-SI and persistent memory
SR-SI turns AI memory from prompt history into a persistent, auditable orientation system for long-running work.
AI Workflow Architecture
AI workflow architecture matters because AI adoption fails when tools know the task but not the operating context around it.
memory retrieval context drift sr si ai assisted development ai workflow governance ai implementation
Chapter thesis
AI creates leverage only when memory, context, and governance are designed into the workflow.
Chapter guide
Context architecture
AI context fails when information is unstructured, diluted, or unfocused; the fix is orientation architecture, not larger prompts.
AI-assisted development
AI-assisted development works when discovery, specs, codebase context, and architectural judgment stay coherent across sessions.
AI workflow governance
AI governance has to shape identity, boundaries, risk, and adoption behavior before tooling scales bad habits.
Organizational memory
The same memory discipline that keeps AI coherent can help organizations preserve context, judgment, and continuity over time.
Applied AI integration
AI integration succeeds when teams build orientation, readiness, workflow maps, and implementation capacity before buying tools or training people.
Core essays
Encyclopedia vs index: why longer prompts don't fix AI
The problem isn’t how much context you give AI — it’s how findable that context is. Better outputs come from better information architecture, not longer prompts.
SR-SI: The methodology that gives AI persistent memory across any long-running project
SR-SI V2 is live: 106x Token Coherence improvement, near-zero marginal upkeep through agent-operated maintenance, and new findings on functional identity over long-running workflows.
SR-SI: the methodology for AI that maintains context across a full product build
An accessible explanation of SR-SI as a context architecture for maintaining AI coherence across long-running product builds, teams, and sprint cycles.
Supporting essays
The AI taskforce problem: why most companies form them wrong
Most companies form AI taskforces around tools and policies before diagnosing where knowledge lives and where friction concentrates. That sequencing mistake is why many AI efforts underperform.
What if your company had a nervous system?
If SR-SI can make AI agents stop forgetting, the larger question is whether the same structure can make an organization hold context across time.
Spec-first is anxiety management
In unknown territory, comprehensive specs don’t reduce risk — they manufacture false certainty. Build small tests, document learning, and iterate with cheap context re-entry.
Further reading
Context decay in AI isn't a model problem. Here's what it actually is.
Context decay usually comes from the way relevant information gets diluted inside long sessions, not from the model suddenly becoming less capable.
Why AI context degrades — and the architectural fix that actually works
AI context drift isn’t a model limitation—it’s an architectural failure. SR-SI replaces brute-force context with indexing, enabling persistent coherence across long-running projects.
SR-SI context savings scale progressively
SR-SI doesn’t just reduce AI re-orientation — it scales with repo complexity. Small repos save 10–20%. Large repos can reach 40–60% when governance stays tight.
What Deserves to Persist is ready
The full essay connects earned memory, SR-SI, ephemeral software, organizational memory, and the question of what the digital self should be allowed to preserve.
The difference between a team that uses AI and a team with AI workflow. It's not subtle.
A workshop-facing piece on why scattered AI use only becomes team capability when prompts, tickets, product decisions, and review habits share one record.