· 5 min read
How a Sales Team Automated 80% of Meeting Scheduling with 4 API Calls
An SDR team recovered 13+ hours per day by replacing manual scheduling with 4 API calls. Here's the workflow, the math, and the code.
· 6 min read
Agent-to-Agent Scheduling: What Happens When Both Sides Are Autonomous
When two AI agents negotiate a meeting, you get distributed systems problems. Here's how protocol negotiation, deadlock avoidance, and fallbacks work.
· 6 min read
The Build-vs-Embed Decision for AI Scheduling
A decision framework for developers: when to use calendar APIs directly, when to build your own time logic, and when you need scheduling infrastructure.
· 7 min read
Building an AI Recruiting Coordinator That Schedules Across 6 Time Zones
End-to-end walkthrough: how to build a scheduling agent that coordinates interviews across US, Europe, and Asia using 5 tool calls.
· 8 min read
Scheduling Is Infrastructure, Not a Feature
Payments got Stripe. Messaging got Twilio. Scheduling is still duct-taped together. Here's why it needs dedicated infrastructure.
· 5 min read
Why Your Calendar MCP Server Doesn't Have Locking (And Why It Should)
Every calendar MCP server today is a CRUD wrapper with no locking. AI agents make this dangerous. Here's why.
· 4 min read
Building Your First AI Scheduling Agent with Temporal Cortex
Step-by-step tutorial: install the MCP server, connect Google Calendar, and schedule a meeting in under 5 minutes.
· 3 min read
What is TOON? A Token-Efficient Format for AI Agent Data
TOON reduces calendar payloads by ~40% vs JSON. Here's how it works, when to use it, and why it matters for AI agent context windows.
· 5 min read
The Double-Booking Race Condition (And How 2PC Prevents It)
Two AI agents, one free slot. Without locking, both book it. Here's the race condition no other calendar MCP server prevents.
· 5 min read
LLM vs Truth Engine: 5 RRULE Expansions Side-by-Side
We ran 5 real-world RRULEs through frontier LLMs and the Truth Engine. The results show why calendar math needs computation, not prediction.
· 3 min read
Why AI Agents Fail at Scheduling (And How to Fix It)
LLMs score below 50% on temporal reasoning. Here's why calendar tools need deterministic computation, not more prompting.