
Build vs. Buy an AI Agent: The Honest Decision Tree
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.

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.

The next security battleground isn't human passwords — it's who owns an autonomous agent's credentials when it acts on your behalf.

AGI-ladder charts are fun philosophy and useless procurement docs. Here's a practical framework for LLM model selection based on capability and cost.

Retrieval-augmented generation and enterprise AI search get conflated constantly. Here's the actual difference — and when to use RAG instead of plain search.

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.

LLM coding tools quietly omit rate limits, logging, and error handling — not because they can't, but because nobody named them. Here's how to prompt around it.

Task-completion scores hide silent failures. Here's a working method for AI agent evaluation using judged rubrics, adversarial cases, and regression suites.
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