
Multi-Agent Systems Are Coordination Problems, Not Model Problems
The bottleneck for multi-agent setups isn't a smarter model — it's shared state, clean handoffs, and knowing who owns the calendar. Coordination is the real work.

The bottleneck for multi-agent setups isn't a smarter model — it's shared state, clean handoffs, and knowing who owns the calendar. Coordination is the real work.

Move past one-off prompts. Build a persistent Tamaton agent that watches your inbox, files, and calendar, then acts on a schedule with human checkpoints.

A scheduling bot books slots. A real AI calendar agent remembers, decides, and negotiates. Here's how to design the second one.

A product deep-dive on the permission, preview, and rollback design that makes an AI email agent safe to actually act — not just suggest.

A field-tested breakdown of which inbox tasks AI agents handle reliably today, and where human review still isn't optional.

A field guide to separating agents that actually finish work from the reasoning-superhero hype — and the boring, bounded jobs they quietly win at.

Most scheduling agents fail because they treat the calendar as slots to fill instead of constraints to reason about. Here's what real calendar reasoning requires.

Treat every agent action as untrusted by default. Here's how to build permission boundaries around inbox, files, and calendar instead of handing out blanket access.

A single-task AI agent and a goal-directed agentic system are not the same thing. Here's the capability boundary, and what each is actually good for.

Single-turn benchmarks miss what matters. Here's a practical eval harness for agents that move data across email, docs, sheets, and calendar.

A practical guide to what AI scheduling agents actually need — timezone handling, conflict resolution, and intent inference — and where today's automations break.

A step-by-step guide to building a custom email agent that reads, labels, drafts replies, and schedules follow-ups using Tamaton's unified context.
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