
Agent Strategy Decks Are Everywhere. Working Agents Aren't.
Every vendor shipped a 2026 AI agent strategy framework this quarter. The agents that survive real work share three unglamorous traits — and break in predictable ways without them.

Every vendor shipped a 2026 AI agent strategy framework this quarter. The agents that survive real work share three unglamorous traits — and break in predictable ways without them.

Folders and filters were a workaround for bad search. In an AI-native stack, your inbox is a messy index — and treating it that way changes triage, search, and follow-up.

A hallucinated sentence is awkward. A hallucinated formula is expensive. Here's a taxonomy of where generative AI fails in structured work — and the guardrails that catch each type.

Public policy researchers are arguing about whether LLM-assisted coding counts as method. Their objections — bias, reproducibility, opaque provenance — apply to your Tuesday afternoon analysis too.

A practical eval playbook — golden sets, LLM-as-judge with guardrails, and regression tracking — for teams shipping AI, not writing papers.

New research suggests generative AI speeds work up while quietly reducing what people actually learn. Here's how to restructure AI-assisted work so speed doesn't cost you understanding.

A field guide to grounding failures — fabricated npm packages, invented spreadsheet columns, phantom calendar events — and the design patterns that catch them before they ship.

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.

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.
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