
Spreadsheets as Agent Workspaces: Beyond the Formula Bar
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.

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.

Public benchmarks rarely predict real performance. Here's how to build a task-specific eval harness from your own emails, docs, and spreadsheets.

The context window is a scratchpad, not storage. Here's how to architect external memory layers for durable, reliable agent state.

A diagnostic framework for the quiet retrieval failures that degrade RAG quality — from chunking strategy to embedding mismatch.

Retrieval failures aren't one bug — they're three. A diagnostic framework for isolating chunking, embedding, and reranking problems instead of guessing.

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