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As AI agents move from demos into daily operations, governing them gets harder: who decides what an agent can reach, and how do you enforce it when the agent's own code could be manipulated? We tested this using NVIDIA OpenShell, an open-source runtime for running autonomous agents in isolated sandboxes governed by declarative policy.
For a few years now, “AI in the SDLC” meant autocomplete. A smarter suggestion in the editor, a faster way to write a unit test, a shortcut around boilerplate. Useful, but marginal. It shaved minutes off tasks that were already fast.
For firms that provide fund administration, accounting, compliance, reporting and other back-office services to private-capital managers, artificial intelligence offers more than a cost-reduction opportunity. Properly deployed, it can materially improve client satisfaction, deepen relationships and become a powerful new-business advantage.
For years, technology within private equity was largely viewed as a cost center. IT investments focused on keeping systems running, reducing infrastructure costs, and supporting portfolio company operations. Today, that mindset is changing.
Every HVAC and plumbing distributor runs three distinct pricing models at once:
Every B2B Commerce platform has AI agents now. They demo well, they are getting easier to build, and a single agent pointed at one job can do genuinely useful work.