· 5 min read
How Tamaton's Search Reads Your Files Before You Ask
A concrete look at building RAG over your own email, docs, and drive — and why retrieval quality, not model size, decides whether AI knowledge work is actually useful.
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
Reason, Plan, Act — Then What? Evaluating Agents That Do Work
Reasoning and planning demos are easy. Proving a multi-step agent actually finished the job — correctly, safely, once — is the unsolved part. Here's how to measure it.
· 5 min read
Stop Calling It an Agent: A Working Taxonomy for 2026
"Agent" now means everything from a system prompt to a six-hour autonomous process. Here's a five-tier taxonomy based on autonomy, state, and blast radius — and why most agents on sale are tier one.
· 5 min read
Fine-Tuning vs. In-Context Learning: When to Bother
Most teams reach for fine-tuning when a decent retrieval pipeline and a tight prompt would have been cheaper, faster, and far easier to change tomorrow. Here's how to tell the difference.
· 5 min read
Your Inbox Is the Best RAG Corpus You Already Own
Email is a timestamped, attributed archive of how your org actually decides things. Here's how to make it a retrieval corpus without leaking data or resurfacing stale threads.
· 5 min read
Designing an Inbox an Agent Can Actually Read
Email was built for eyeballs, so agents waste context untangling HTML soup and quoted replies. Here's what an inbox designed for machines looks like — and why the human UI should be just one renderer.
· 4 min read
Most AI Productivity Tools Don't Save Time. Here's the Test.
A defensible framework for measuring whether an AI tool actually returns hours — including verification overhead, context-switching, and the trust tax nobody counts.
· 5 min read
Guardrails Beat Bigger Models: Where Agents Put Things Down
The most reliable multi-step agents aren't the smartest — they're the ones handed structured places to stash state, permissions, and checkpoints. Here's how to build those places.
· 5 min read
RAG Isn't Search: Why Your Retrieval Keeps Lying to You
Most RAG hallucinations aren't model failures — they're retrieval failures. Here's a breakdown of chunking, recency, and permission bugs, plus fixes you can defend in code review.
· 5 min read
Why AI Agents Need Isolation, Not Just Permissions
Permissions decide what an agent may touch. Isolation decides what happens when it gets tricked. Here's how to design agent access like a network DMZ.
· 5 min read
When Agents Collide: What Anthropic's Clashing Bots Teach Us
Identical agents don't cooperate by default — they clash, collude, and coordinate in ways nobody specified. That's an architecture problem, and it has fixes.
· 4 min read
Federated LLMs: Why Your Data Doesn't Have to Leave the Building
Federated training lets models learn across organizations without pooling sensitive data. Here's what that shift means for privacy-preserving AI and knowledge work.
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