
RAG Is Not a Search Bar: Retrieval for Real Knowledge Work
Demo RAG retrieves from one clean corpus. Real work means retrieving across email, docs, spreadsheets, and calendar — where context is stale, duplicated, and contradictory.
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Practical writing on productivity, AI, and building software.

Demo RAG retrieves from one clean corpus. Real work means retrieving across email, docs, spreadsheets, and calendar — where context is stale, duplicated, and contradictory.

A practical permissions model for AI agents where every action — reading email, editing a doc, calling an API — is a privilege that must be earned, not assumed.

AI agents can already do the work. The bottleneck is authorization — and most teams are quietly lending agents a human's credentials and hoping for the best.

Assistants don't fail at memory because models forget. They fail because memory gets stored as a text blob instead of a governed store with recency, decay, provenance and conflict rules.

A practical spec for AI agent observability: what to capture in every trace — tool calls, token spend, decision forks, and the silent failures nobody logs.

You don't need an ML platform to measure AI quality. A well-structured sheet turns vibes-based prompting into something you can actually score and improve.

Inside the eval harness we run on inbox triage, doc drafting, and calendar scheduling — the golden sets, the scoring rubrics, and the failure modes we refuse to ship.

Shared service accounts make agent activity untraceable. Here's how scoped per-agent identities, capability grants, and audit trails fix that.

Tamaton search spans mail, docs, and files — but every hit is checked against the caller's grants at query time, so an over-eager agent can't retrieve what it was never allowed to see.

New adoption data shows the biggest productivity gains go to people using purpose-built AI tools, not general chatbots. The difference isn't model quality — it's context and integration.

A field guide to choosing between deterministic automation and goal-seeking agents — with a decision tree, cost math, and the honest cases where a plain script still wins.

Model quality isn't your bottleneck anymore — context portability is. We map where the time actually goes in AI-assisted work, and what to fix first.
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