What we're hearing about AI in PE on calls this quarter: 1. Funds are putting their own AI engineers inside portcos. 2. Portco CFOs see more pull requests than ever, but the codebase isn't getting better. 3. Dev vendors that won't use AI are being asked why they're still being paid. 4. Funds want AI work priced against EBITDA upside. They ask about revenue per FTE. 5. Every portco runs its own AI experiment. None of it makes it to the next company. 6. New board seats come with one job: choose the AI vendors. The funds making progress are getting one workflow working at a time.

There are seven main steps for your company to go from tool adoption to agentic org. Most companies sit on step one, shopping for step seven technology. They need step four discipline. Step one: tool adoption. AI tools exist across the org, humans run each one. Developer closes the lid, automations die. Somebody goes on vacation, the workflow goes with them. Step two: context layer. Knowledge graphs per department, each behind its own API gateway. Sales queries engineering through an API. Separated domains keep agents from hallucinating across departmental boundaries. Step three: harness engineering. Build evaluation criteria, routing logic, and quality gates before you pick a model. The LLM comes in last. Your harness is your IP and your capex. The model underneath is a commodity you swap when something better ships. If changing the LLM breaks your system, you built a dependency, not a platform. Step four: go headless. Host every automation outside a laptop. It fires on schedule, on conditions, without a human logging in at 9 AM. Companies stall here because nobody forces the migration from “script on my desktop” to “service that runs on its own.” Step five: governance. RBAC on every knowledge structure from day one. TTL on every agent. Dead agents are tech debt with active API keys. Past 100 tools, you need lifecycle management. Retire anything that hasn’t run in 90 days. Deprecate anything whose eval scores drop below threshold. Step six: agent-to-agent. Stitch headless instances together within a value chain. Agent A finishes, hands off to Agent B. An LLM sits between them as a quality gate, judging every handoff before it passes through. Step seven: step functions. Orchestrated, semi or fully automated workflows. Agents handle volume. Humans handle exceptions. The org runs on agent output. We walked a VP of Engineering through all seven. Told him: “You’re on step one. But over a hundred tools means step four is closer than you think.”

I see AI agents fail inside big companies in two ways. The first is obvious. The person who built it leaves, and the agent goes with them because it only ever ran on their laptop. Nobody hosted it because the company never decided where agents should run. The second is easier to miss. The agent gets hosted and runs for months without anyone checking it. The model updates or a system it reads from changes, but it keeps answering just as confidently. Before a company ships its second agent, I want these written down: > Where every agent runs. One runtime for the whole company > Who registers new agents > Who owns each one, by name > Which tools and data it can access, and who approved that access > When it gets reviewed, and when it gets retired None of this is exciting. But if the business is going to rely on an agent next year, someone needs to be responsible for it after the person who built it moves on.