· 5 min read
AI Agents Are a New Identity Class. Treat Them Like One
Every agent touching your inbox, files, and calendar is a non-human user with credentials and blast radius. Most orgs provision them like scripts and hope for the best.
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
Why Your Agent Forgets: A Practical Guide to Agent Memory
Memory, not model size, decides whether an agent finishes a task or loops forever. Here's how planning, working memory, and retrieval actually fit together.
· 5 min read
Your Inbox Is the Best RAG Corpus You'll Ever Have
Mailboxes, files, and calendars are structured, timestamped, and permissioned — which is why naive vector search fails on them. Here's a retrieval design that works.
· 5 min read
Why Time-Series LLMs Hallucinate Forecasts (And When Not to Trust Them)
A practitioner's guide to when LLM-based forecasting earns its keep and when it's confidently extending a trend line into thin air.
· 4 min read
Aggregate Scores Lie: What EduFairBench Teaches AI Evaluation
Averaged benchmark scores hide the subgroup failures that break real workflows. Here's how to build disaggregated, reproducible evals for triage, summaries, and spreadsheets.
· 5 min read
One Model to Rule Them All Is a Myth: Routing for Real Work
Chasing the single 'best' LLM is a losing game. Here's a practical framework for routing each task — code, extraction, drafting, reasoning — to the model that actually fits.
· 5 min read
From Chatbots to Reasoning to Agents: What Changed in 2025
Reasoning models solved 'can it answer.' Agentic training moved the hard part to 'can it act reliably' — and that shift changes how everyday work gets done.
· 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
Small Models, Big Inbox: When Not to Reach for a Frontier LLM
For classifying, routing, and summarizing email, a small fast model usually beats a frontier one on cost and latency. Here's how to choose per task instead of per vendor.
· 5 min read
Why Rigid APIs Break Agents — and Email Doesn't
Strict API contracts turn small agent mistakes into hard failures. Email tolerates ambiguity, supports clarification, and keeps the whole exchange on the record.
· 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.
· 5 min read
The Agent-to-Agent Email Problem Nobody Has Solved
When agents email agents, threads, signatures and pleasantries become expensive noise. Here's the case for a structured A2A email layer with provenance, intent tags, and evaluation built in.
Get started
Reserve your @tamaton.ai email
Claim your address before someone else does — free to start, with an AI-native inbox built in.