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Imagine a self-driving car whose cameras are smeared with mud and its maps are months out of date. The software can still turn the wheel, but you probably wouldn’t let it merge onto a busy highway. Would you trust it to navigate safely? Probably not. Yet deploying AI agents without deeply integrating them into a system of record (your CRM, ERP, HCM, etc.) is much the same — the agent is essentially driving blind. In the rush to add AI “copilots” and chatbots to enterprise software, savvy CTOs and architects are realizing that native agentic SaaS platforms — where AI agents are embedded inside enterprise workflows — can only succeed if they are grounded in the digital spine of the business: its systems of record.
Press e…
Member-only story
18 min readJust now
–
Imagine a self-driving car whose cameras are smeared with mud and its maps are months out of date. The software can still turn the wheel, but you probably wouldn’t let it merge onto a busy highway. Would you trust it to navigate safely? Probably not. Yet deploying AI agents without deeply integrating them into a system of record (your CRM, ERP, HCM, etc.) is much the same — the agent is essentially driving blind. In the rush to add AI “copilots” and chatbots to enterprise software, savvy CTOs and architects are realizing that native agentic SaaS platforms — where AI agents are embedded inside enterprise workflows — can only succeed if they are grounded in the digital spine of the business: its systems of record.
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Figure 1 : from System of Records to Self-Driving SaaS
In this post, we explore the evolution from systems of record to systems of agency, explain what an agentic SaaS architecture looks like, and argue why deep integration with core data and processes is non-negotiable. We’ll use clear analogies and real examples (Order-to-Cash, Procure-to-Pay) to illustrate how self-driving SaaS works in practice, and we’ll outline a roadmap from today’s copilots toward truly autonomous enterprise software.