· 5 min read
How We Let an Agent Touch Your Inbox Without Losing Trust
A product deep-dive on the permission, preview, and rollback design that makes an AI email agent safe to actually act — not just suggest.
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#search#security#spreadsheets#startups#storage#tamaton#unified-workspace#workflow#workflows
· 4 min read
The Agent Reliability Gap: Why 90% Isn't Good Enough
Multi-step agents compound their error rate — a 95%-accurate step becomes a coin flip after 15 actions. Here's the math and how to design around it.
· 4 min read
How to Evaluate an AI Feature When 'It Feels Better'
A practical eval playbook for AI-assisted knowledge work: build golden sets, run LLM-as-judge checks, and catch regressions without a research team.
· 4 min read
One Agent or Many? A Sober Take on Multi-Agent Hype
Multi-agent systems are trending, but most productivity work is better served by one well-scoped agent with good tools. Here's when orchestration actually earns its keep.
· 4 min read
How Tamaton Grounds Agent Actions in Your Real Data
A concrete look at how shared context across inbox, docs, sheets, and calendar gives AI agents verifiable grounding — and fewer confidently wrong actions.
· 4 min read
Project Memory: The Missing Layer in AI Assistants
A bigger context window won't fix a forgetful assistant. Persistent, structured project memory is what turns chat toys into long-running collaborators.
· 4 min read
Automating Inbox Triage With Agents: What Actually Works
A field-tested breakdown of which inbox tasks AI agents handle reliably today, and where human review still isn't optional.
· 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.
· 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.
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