We built a three-agent AI workforce in early 2026. By spring, it had grown to ten agents running daily on schedules. Here is what broke when we scaled, the governance we had to build, and the honest lessons from six months of operating autonomous agents in production.
Read moreRoughly nine out of ten AI proofs of concept never become a service that runs on its own schedule. The gap between a tidy demo and a system that survives midnight alerts is a set of disciplined engineering decisions -- not a talent problem.
Read moreA single mis-priced transaction can cost a firm millions; the maker-checker pattern gives you a reliable second opinion for AI-driven pipelines. We show how pairing deterministic code with an LLM auditor catches errors that each alone would miss.
Read moreWhen deterministic ETL pipelines start to crumble under ambiguous data and regulatory pressure, agentic workflows can restore reliability without a full rewrite.
Read moreLangGraph is gaining real adoption for agentic AI workflows. But for most teams evaluating it, the question is not how to build a pipeline -- it is whether LangGraph is the right architecture for their problem, and what it actually takes to run in production.
Read moreAgentic AI is everywhere in vendor decks, but most teams cannot explain what it actually means for their data operations. This guide cuts through the hype with a practitioner's definition, a real architecture example, and a framework for deciding whether you need outside help.
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