
Model Selection in 2025: When Smaller Beats Frontier
A practical framework for routing tasks to small, fast models or frontier reasoning models based on latency, cost, and failure cost.

A practical framework for routing tasks to small, fast models or frontier reasoning models based on latency, cost, and failure cost.

A quantitative look at how context window limits quietly degrade agent performance on email threading and document synthesis—and what to do about it.

Stop defaulting to the largest frontier model. Use a cost-latency-quality matrix to route each task to the right LLM and cut spend without hurting output.

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.

Why the humble spreadsheet is one of the best environments for AI agents to read, write, and verify data — and why grounded tabular reasoning beats free-text prompting.

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.

Memory isn't one feature. A practical breakdown of episodic, semantic, and working memory for AI agents — and how to wire them into real workflows.

LLMs reason poorly over raw grids because cells lose their meaning. Here's why ai spreadsheet analysis breaks down — and how structure fixes it.

A practical guide to what AI scheduling agents actually need — timezone handling, conflict resolution, and intent inference — and where today's automations break.

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.
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