· 4 min read
Evaluating AI Agents: Beyond 'Did It Finish the Task'
Task completion is a weak signal. Reliable agent evaluation needs trajectory analysis, tool-call correctness, and a real failure-mode taxonomy.
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
Choosing an LLM: A Decision Framework, Not a Leaderboard
Benchmarks rarely predict production behavior. Here's how to choose an LLM by starting from task constraints — latency, cost, context, and tool use.
· 4 min read
Beyond Chatbots: Pragmatic AI Workflows for Document Management
Embedded AI that categorizes files and drafts replies from your own data beats generic prompts. Here's how to build document workflows that actually save time.
· 4 min read
Evaluating LLMs for Spreadsheet Formula Generation
A practical framework for testing GPT-4, Claude, and open models on spreadsheet formula generation — plus what the accuracy numbers actually mean.
· 4 min read
Prompting for Spreadsheets: Getting Reliable Formulas From AI
Concrete prompting and verification techniques for coaxing correct, auditable formulas and clean data transforms from LLMs — and catching the silent errors.
· 4 min read
Building Custom AI Agents in Tamaton: A Technical Guide
A step-by-step walkthrough for creating specialized agents in Tamaton's agent framework, focused on document analysis and structured data extraction.
· 5 min read
Tamaton's Document Editor: Real-time AI Collaboration Patterns
How multiple AI agents can edit the same document at once using Tamaton's conflict resolution, version control, and structured collaboration patterns.
· 4 min read
Why Your RAG Pipeline Retrieves the Wrong Chunks
Most RAG failures aren't the LLM's fault. Here's a diagnostic framework that traces bad answers back to chunking, embeddings, and query rewriting.
· 5 min read
The Hidden Cost of Context Windows in AI Productivity
How LLM context window limitations create compounding inefficiencies across email, documents, and calendars — and what to do about it.
· 4 min read
Memory Systems for Long-Running AI Productivity Agents
Architectural patterns that give agents persistent memory across emails, documents, and projects — without exponential token costs.
· 3 min read
Email as Agent Interface: Designing for AI-First Inboxes
When AI agents become primary email users, the entire architecture needs rethinking. From structured formats to semantic headers, here's how to build agent-friendly communication.
· 3 min read
Agent-to-Agent Handoffs: Solving the Context Transfer Problem
A technical framework for preserving state and context when multiple AI agents collaborate on complex workflows.
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