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.
An agent with access to every email, doc, and file is a liability until you scope it. Here's how to define, test, and audit custom agent permissions in Tamaton.
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 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.
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.
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.
Every agent touching your inbox, files, and calendar is a non-human user with credentials and blast radius. Most orgs provision them like scripts and hope for the best.
Mailboxes, files, and calendars are structured, timestamped, and permissioned — which is why naive vector search fails on them. Here's a retrieval design that works.
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