· 5 min read
Chatbot vs. Agent: The Line That Actually Matters
A chatbot returns a response. An agent decides and executes a sequence. Confusing the two is why so many 'agentic' rollouts quietly stall out.
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
One Model to Rule Them All Is a Bad Default
Picking a single LLM for every task leaves capability and money on the table. Route by task instead: cheap triage, strong drafting, dedicated verification.
· 5 min read
Your Inbox Is a Database. Query It Like One.
Email isn't a list of messages — it's an unindexed database you never designed. Here's how to query it in natural language without hallucinating your way into a bad reply.
· 5 min read
When to Trust an LLM: Lessons from Radiologists Who Don't Get Fooled
Medical research on why some experts resist bad AI advice — and how to turn those findings into a practical framework for judging LLM reliability in your own work.
· 6 min read
Nobody Evaluates Their Prompts. Here's a 30-Minute Fix.
A practical walkthrough for building a 40-example eval set in a spreadsheet — plus the LLM-as-judge traps that make most accuracy numbers meaningless.
· 5 min read
How Tamaton Agents Finish Multi-Step Tasks Without Losing the Thread
A concrete walkthrough of building a Tamaton agent that triages inbox, drafts replies with real context, and books follow-ups — with a human holding the approval button.
· 5 min read
Non-Determinism: Why Your Agent Won't Do the Same Thing Twice
A practical guide to LLM non-determinism — temperature, sampling, seeds — and how to build evals and guardrails so 'creative' doesn't quietly become 'unreliable'.
· 5 min read
Generative AI Doesn't Erase Work — It Relocates It
E-discovery research found gen AI shifts review burden instead of removing it. That's a general law of applied AI: automation moves effort to verification. Design for it.
· 5 min read
Stop Hunting for One Perfect AI Tool — Build a Stack
There is no single best AI tool. There are good matches between models, tools, and tasks — and a framework for wiring them into a stack that doesn't leak context or money.
· 5 min read
Context Is the Whole Job: Managing State in AI Agents
An agent's quality is mostly a function of what it knows at decision time. Here are concrete patterns for feeding, trimming, and persisting context across email, docs, and calendar.
· 5 min read
Agents Don't Fail at Reasoning. They Fail at Hour Three.
Long-running AI agents rarely collapse because they can't think. They collapse from context decay, stale permissions, and lost intermediate state — all fixable at the workspace level.
· 4 min read
Most 'AI Agent Use Cases' Are Cron Jobs With Anxiety
A practical test for when to use an AI agent versus a plain script in an LLM costume. Three questions decide it: ambiguity, branching, and recoverable failure.
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