The industry keeps drafting new agent-to-agent handshake specs. Meanwhile, email quietly ships addressable identity, async delivery, and threading — the interoperability layer AI agents already have.
Email is the messiest RAG corpus you'll ever ingest. Here are the chunking, dedup, and recency choices that make inbox retrieval trustworthy.
The best production agents are boring: deterministic software that calls an LLM at a few deliberate points—not a chatbot with root access.
The bottleneck in production AI agents isn't model IQ — it's context engineering, tool reliability, and memory. Here's what actually breaks and how to fix it.
Prototyping a workflow agent is easy. Running one in production is where auth, retries, evals, and oversight quietly eat your quarter. Here's how to decide.
A practical model-selection framework for applied knowledge work — covering cost, context, latency, tool use, and failure modes instead of leaderboard hype.
LLMs feel like a slot machine with no pity system. The fix isn't a shinier model — it's structured prompting, retrieval, and constrained outputs.
New research shows LLMs hallucinate when forecasting time-series data. Here's why that matters before you point one at your spreadsheet.
A concrete look at how a unified data layer lets AI cite the actual email or spreadsheet cell instead of paraphrasing from memory.
New cognitive-science comparisons show LLMs write like humans through decidedly non-human machinery. Here's what that means for the work you hand them.
AGI-ladder charts are fun philosophy and useless procurement docs. Here's a practical framework for LLM model selection based on capability and cost.
When agents send and reply at machine speed, triage becomes a routing and evaluation problem. Here's how we approach ai email management built for bots, not just humans.
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