· 5 min read
Most AI Agents Nobody Needs: The Utility Test
A practical framework for deciding when an autonomous agent actually beats a prompt or a plain feature — and when it's just expensive theater.
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
How to Choose an LLM in 2026: A Buyer's Map
A practical selection matrix across reasoning, coding, latency, and cost — plus why routing one model per task beats crowning a single winner.
· 4 min read
Fine-Tuning vs Prompting for Knowledge Work in 2026
LoRA and lightweight fine-tuning are cheaper than ever, but for most inbox and document tasks, prompting plus retrieval still wins. Here's the decision tree.
· 4 min read
RAG for Inboxes: Grounding Email Replies in Real Context
Email is a messy, threaded corpus. Good retrieval design — thread chunking, dedup, recency weighting — decides whether AI replies are trustworthy.
· 4 min read
The Inbox Is the Real Agent Benchmark
Email is the hardest test for an AI agent: ambiguous intent, irreversible actions, and real trust. Here's why most demos quietly avoid it.
· 4 min read
Why Multimodal Benchmarks Still Fail Agentic Work
Most multimodal benchmarks test isolated perception, not the chained document-to-action tasks agents actually perform. Here's what better evaluation looks like.
· 4 min read
Why AI Agents Aren't Coworkers — and Why That Matters
Calling AI agents 'digital employees' quietly erodes human accountability. Treat them as orchestrated, audited tools instead — here's how and why.
· 4 min read
Why Agents Fail at Long Tasks (And How to Fix Drift)
A diagnostic framework for why agentic workflows degrade over multi-step tasks — context loss, tool errors, and goal drift — plus concrete mitigations.
· 4 min read
Difficulty-Aware Prompting: When Chain-of-Thought Hurts
Reasoning depth should scale with task difficulty. Here's when chain-of-thought helps, when it hurts, and how to spend reasoning tokens wisely.
· 4 min read
Prompting Is Becoming Spec-Writing
The clever one-liner prompt is fading. Durable AI work needs specifications: constraints, examples, and acceptance criteria you can reuse.
· 4 min read
AI Workflows vs. AI Agents: When to Use Which
A decision framework for choosing constrained AI workflows over open-ended agents — and the specific conditions that justify going agentic.
· 4 min read
I Audited a Week of 'AI Productivity' — What Actually Saved Time
A rigorous time-and-error log of AI in real knowledge work — separating genuine wins from rework and the hidden review tax.
Get started
Reserve your @tamaton.ai email
Claim your address before someone else does — free to start, with an AI-native inbox built in.