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Deploying AI agents into real-world operations is not about prompting models — it’s about engineering. When an agent processes sensitive documents, interacts with customers, touches compliance workflows, or routes operational work, it behaves like any other production system and must be treated as one. Safety, reliability, and observability aren’t optional extras; they are foundational requirements. Most of the failures we see in mid-market deployments can be traced back to o

Most mid-market companies suspect they’re behind on AI. What they don’t realize is where  they’re behind — and why it matters. When people hear “AI,” they think of futuristic technologies, massive data platforms, chatbots, or expensive enterprise initiatives designed for Fortune 500 budgets. They imagine complicated machine-learning projects, giant models, heavy infrastructure, and long implementation timelines. It feels distant from the daily reality of running operations in

Most organizations don’t realize how much time they lose to operational drag. Not because they aren’t smart, resourceful, or structured, but because the cost of manual work hides itself inside familiar routines. These workflows feel small in isolation: reviewing a form, routing an email, copying information from one system to another, assembling a packet, responding to a request. None of them look like a crisis. But repeated hundreds or thousands of times per week across mult

AI is transforming how modern operations teams work, but one of the biggest mistakes companies make early on is assuming they should “automate everything.” Automation doesn’t succeed because an organization goes broad; it succeeds because it starts precise. The real challenge isn’t building the AI—it’s knowing where to use it first . Across mid-market organizations, operational bottlenecks hide inside everyday workflows that rarely get strategic attention. Intake processes, d