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.
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.
Shared service accounts and copied human credentials make AI agents untraceable and over-privileged. Here's what agent-native identity looks like in a real productivity stack.
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.
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.
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