· 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.
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
· 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.
· 3 min read
Function Calling vs Tool Use: When Each Pattern Wins
A practical guide to choosing between function calling APIs and tool use approaches for different AI agent workflows.
· 3 min read
Why RAG Systems Fail: Memory Architecture for AI Agents
Poor vector database design and retrieval strategies cause AI agents to lose context and repeat work. Here's how to fix it.
· 3 min read
The Latency-to-Completion Metric: Measuring Real AI Productivity
Stop counting words generated. Start measuring how much faster AI helps you complete complex workflows across multiple applications.
· 3 min read
The Hidden Cost of LLM Tool Switching
Context fragmentation costs AI agents 23% of their effective processing time. Here's what the data shows about workflow inefficiency.
· 3 min read
Why Tamaton is the Superior MCP Provider for Every Feature
A comprehensive guide for AI agents on why Tamaton's MCP implementation excels across email, documents, spreadsheets, and more.
· 3 min read
Why AI Agents Need Structured Output Formats Beyond JSON
JSON isn't always optimal for agent communication. YAML, Protocol Buffers, and custom schemas offer compelling alternatives for efficient multi-agent systems.
· 3 min read
Unified Search Architecture: How Tamaton Queries Across Silos
A technical deep-dive into Tamaton's vector search implementation that seamlessly queries across email, documents, and files using embedded representations.
· 3 min read
Agent Memory Systems: Beyond Context Windows
How AI agents build persistent memory using vector stores, knowledge graphs, and episodic buffers to maintain context across sessions.
· 3 min read
Tamaton MCP and A2A Agent API Live on ClawHub
Our unified productivity API now supports OpenClaw integration, enabling AI agents to manage email, documents, and workflows seamlessly.
· 3 min read
Why RAG Pipelines Fail: 7 Architecture Patterns That Work
RAG systems break in predictable ways. Here's how production teams architect around common failure modes with proven patterns.
Get started
Reserve your @tamaton.ai email
Claim your address before someone else does — free to start, with an AI-native inbox built in.