· 5 min read
The Audit Trail Problem: Email as Agent Accountability
Enterprises can't deploy agents they can't review. Email threads already give every agent action a timestamped, human-readable, forwardable paper trail.
All#ai#ai-agents#ai-productivity#applied-ai#automation#calendar#context-switching#deep-work#documents#email#engineering#engineering-leadership#invisible-ai#knowledge-work#llm-evaluation#product-updates#productivity#prompt-engineering#remote-work#search#security#spreadsheets#startups#storage#tamaton#unified-workspace#workflow#workflows
· 5 min read
Grounding Is the New Prompting: How to Stop Agents Guessing
Clever prompts don't make agents accurate — verified, fresh sources do. Here are the grounding architecture patterns that keep AI agents honest.
· 5 min read
Keeping LLMs Current: Knowledge Editing vs. RAG
Pretraining freezes your model's worldview on a date that has already passed. We compare knowledge editing research with retrieval as strategies for keeping an AI assistant accurate.
· 4 min read
Why Your AI Agent Fails at the Handoff, Not the Reasoning
Most agent failures aren't reasoning failures — they're broken context handoffs between tools, memory, and steps. Here's where the seams actually tear, and how to reinforce them.
· 4 min read
RAG for Your Own Files: Why Search Is the Hard Part
RAG over your email, docs, and spreadsheets usually fails at retrieval, not generation. Better search and chunking beat a bigger model almost every time.
· 4 min read
Five Questions Before You Pick an LLM for Real Work
A practical model-selection framework for applied knowledge work — covering cost, context, latency, tool use, and failure modes instead of leaderboard hype.
· 4 min read
When LLMs Forecast: Hallucination in Time-Series Models
New research shows LLMs hallucinate when forecasting time-series data. Here's why that matters before you point one at your spreadsheet.
· 4 min read
How Tamaton Grounds Answers Across Inbox, Docs, and Calendar
A concrete look at how a unified data layer lets AI cite the actual email or spreadsheet cell instead of paraphrasing from memory.
· 4 min read
Do LLMs Think Like Us? What the Research Actually Says
New cognitive-science comparisons show LLMs write like humans through decidedly non-human machinery. Here's what that means for the work you hand them.
· 4 min read
RAG Isn't Search, and Search Isn't RAG
Retrieval-augmented generation and enterprise AI search get conflated constantly. Here's the actual difference — and when to use RAG instead of plain search.
· 5 min read
The Metaphor Trap: Why We Keep Explaining LLMs Wrong
Physics and brain analogies make LLMs feel intuitive — and quietly lead us astray. Here's what to reason about instead.
· 4 min read
The Circular Trust Problem: When LLMs Verify LLMs
Using one model to fact-check another can turn errors into a mutual admiration society. Here's how the recursion happens and how to break it.
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