· 4 min read
Most AI Agents Are Demos. Here's What Real Ones Do.
A field guide to separating agents that actually finish work from the reasoning-superhero hype — and the boring, bounded jobs they quietly win at.
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
· 4 min read
Picking the Right Model per Task: A Router's Playbook
Stop chasing one 'best' model. Route each task — drafting, summarizing, spreadsheet reasoning, coding — to whichever model wins on quality-per-dollar.
· 4 min read
Give Your Agent a Login: Custom Agents in Tamaton
A concrete walkthrough of building a scoped Tamaton agent that triages your inbox, drafts replies, and files documents — with its own identity and permissions.
· 4 min read
RAG for Your Inbox: Retrieval Over an Email Corpus
Email is the hardest RAG target there is — threads, quotes, and recency all conspire against you. Here's how to design inbox retrieval that's actually accurate.
· 4 min read
How to Evaluate an AI Agent Before You Let It Touch Your Inbox
A practical evaluation framework — task success, tool-call accuracy, hallucination rate, and recoverability — for deciding whether an agent is safe near your email and calendar.
· 4 min read
The Calendar Is a Reasoning Problem, Not a UI Problem
Most scheduling agents fail because they treat the calendar as slots to fill instead of constraints to reason about. Here's what real calendar reasoning requires.
· 4 min read
LLM Quantization: What You Actually Lose at 4-Bit
A measured look at where 4-bit quantization degrades reasoning and retrieval — so you pick precision by task instead of chasing the smallest model.
· 4 min read
How Tamaton Selects Models Per Task, Not Per Vendor
A routing layer picks the right model for each request — summarization, extraction, or reasoning — so quality and cost stay flexible instead of locked to one provider.
· 4 min read
Evaluating AI Output When 'Looks Right' Isn't Good Enough
Fluent isn't the same as correct. Here's how to build task-level evals, groundedness checks, and regression testing into everyday AI features.
· 4 min read
From Chat to Workflows: What Makes a Stack Agent-Ready
Agent-readiness isn't about bolting on chatbots. It's about exposing structured tools, clean state, and observable actions across email, docs, and search.
· 4 min read
Model Selection for Agents: Match the Model to the Task
One model can't optimally draft, extract, schedule, and search. Here's a practical routing framework for choosing and switching models per task.
· 4 min read
Agentic AI vs AI Agents: Why the Distinction Matters
A single-task AI agent and a goal-directed agentic system are not the same thing. Here's the capability boundary, and what each is actually good for.
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