
Prompt Caching Strategies for Multi-Agent Workflows
A practical guide to building prompt caching layers that cut latency and cost across complex multi-agent orchestrations.

A practical guide to building prompt caching layers that cut latency and cost across complex multi-agent orchestrations.

Bigger context windows don't guarantee better recall. Here's where models actually lose information — and how to structure prompts so they don't.

A technical look at how Tamaton models multi-party scheduling as a constraint satisfaction problem to coordinate meetings across AI agents and humans.

Why keyword search fails in modern workspaces, and how semantic search, metadata, and permission-aware retrieval combine to make file search actually usable.

Chat is great for conversation, but grids give AI agents structured state, auditable steps, and natural human-in-the-loop checkpoints.

Skip the 'long context killed RAG' debate. Here's a practical decision framework based on cost, latency, recall, and freshness.

Chasing an empty inbox fails at scale. Priority-routing and AI-drafted triage workflows beat completion-based goals every time.

Task completion is a weak signal. Reliable agent evaluation needs trajectory analysis, tool-call correctness, and a real failure-mode taxonomy.

A technical guide to converting messy email into accurate calendar events: entity extraction, temporal reasoning, and conflict resolution that actually holds up.

Benchmarks rarely predict production behavior. Here's how to choose an LLM by starting from task constraints — latency, cost, context, and tool use.

A practical framework for testing GPT-4, Claude, and open models on spreadsheet formula generation — plus what the accuracy numbers actually mean.

A step-by-step walkthrough for creating specialized agents in Tamaton's agent framework, focused on document analysis and structured data extraction.
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