We debate the rise of forward deployed engineers and multi-billion-dollar deployment arms from major AI vendors, and we name the tradeoff: faster wins can also create deeper vendor dependency if capacity is not built inside your team. We share the day 100 question to ask before you sign anything, plus a simple operating model for AI governance: assign a named agent manager, give them real hours, train them, and budget for the humans who sustain the system.

The AI agent demo looks magical, and that is exactly why it can become dangerous. We keep watching a familiar cycle: outside experts parachute into a company, build something impressive in weeks, and then a silent countdown starts the moment they leave. Nothing “breaks,” but value slips, costs creep, answers drift, and the business quietly learns to stop trusting what it just paid for.

We dig into why this happens by borrowing a blunt framework: the law of amplification. AI does not automatically fix broken systems. It amplifies what is already true about your organization, including your documentation quality, policies, decision rights, data hygiene, and ability to manage change. We also explain why AI agents are not the kind of classic software you can install and forget. They behave more like a garden, shaped by model upgrades, shifting business priorities, and stale knowledge bases. Without weekly attention, “agent drift” turns yesterday’s alignment into tomorrow’s risk.

If you want enterprise AI that lasts, not just a launch-day headline, listen through to the checklist and put it to work. Subscribe, share this with the person who owns AI rollout, and leave a review with your answer: who runs your agents on day 100?

Stats and resources: https://ai4sp.org/ai-implementation-heresy

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