Operations | Monitoring | ITSM | DevOps | Cloud

The U.S. government has cataloged over 1,700 active AI use cases, and nearly 90% of federal agencies are already using or planning to use AI. The European Commission has disclosed nearly 1,500 AI use cases across EU member states. With over 3,200 combined AI use cases cataloged across the US and EU, public sector IT leaders face an identical roadblock: traditional application delivery controllers were not designed to parse or throttle Layer 7 LLM payloads, leading to backend GPU exhaustion.

Let’s say your team shipped a support agent last quarter. The launch demo went well, stakeholders were pleased, and everyone moved on. A few months later, things start to look off. Summaries of long conversations are truncated, and monitors show latency spikes on tool calls to the billing API. Your team’s first instinct is to ship fixes such as tweaking prompts or upgrading the model.

A few months ago, Darragh Curran, CTO at Fin (formerly Intercom) set a public goal to double engineering productivity and nearly tripled it instead. They did so by pulling a few levers: AI writing code at scale, building an AI-driven PR review system, leveraging observability as a trust mechanism, and with leadership becoming more hands-on through the transition.

AI ticketing puts machine learning and language models to work on the IT help desk. An AI ticketing system reads tickets, sorts them, routes them, drafts replies, and resolves routine requests without a technician.

Enterprise AI agent programs overspend because leaders optimize token costs, which represent just 20-25% of variable run costs. Human oversight accounts for 70-75%. The real fix is reducing exception rates and embedding governance directly into agent execution from the start.