
How to Evaluate an AI Inbox Assistant Before You Trust It
A concrete eval rubric for email AI — measuring intent precision, action safety, and hallucinated commitments — plus test cases you can run today.

A concrete eval rubric for email AI — measuring intent precision, action safety, and hallucinated commitments — plus test cases you can run today.

Frontier models aren't always the answer. For inbox and search work, routing to small fine-tuned models is quietly becoming the default architecture.

A concrete eval methodology for action-taking agents: measure task success, failure recovery, and over-action risk before you hand over the keys.

Email's threads, recipients, and async replies aren't legacy baggage — they're a proven control surface for AI agents. Here's why the inbox is great agent interface design.

A step-by-step guide to building a custom email agent that reads, labels, drafts replies, and schedules follow-ups using Tamaton's unified context.

An architecture guide for AI systems that classify email by learning from patterns over time, rather than judging each message in isolation.

Stop treating email as a message stream to triage. For agents, the inbox is queryable state — structured, indexed, and ready to act on.

The highest-ROI AI in your inbox isn't drafting replies — it's routing, prioritizing, and summarizing. Here's the architecture to build it.

A practical rubric for judging email AI on triage precision, false-archive rate, and draft acceptance — instead of vibes.

Chasing an empty inbox fails at scale. Priority-routing and AI-drafted triage workflows beat completion-based goals every time.

A technical guide to converting messy email into accurate calendar events: entity extraction, temporal reasoning, and conflict resolution that actually holds up.

How LLM context window limitations create compounding inefficiencies across email, documents, and calendars — and what to do about it.
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