Over 95% of enterprise agentic AI usage comes from third-party autonomous coding agents and low-code platforms. Custom, first-party agent development represents a tiny fraction (2%), revealing a clear market preference for adopting ready-made solutions over building from scratch.

Previously, building bespoke software for niche internal problems was too expensive. AI agents dramatically lower this cost, allowing companies to create custom-fit solutions for 99% of their problems, ending the era of contorting workflows to fit generic, off-the-shelf tools.

DoorDash is building its own AI agent while allowing third-party agents like Muse to operate on its platform. This dual strategy exposes a core conflict for businesses: build a proprietary agent to own the customer relationship and data, or risk being disintermediated by more universal, external agents.

Don't build custom agent infrastructure; major tech players will provide powerful, accessible platforms. Your competitive advantage lies in the difficult work of curating proprietary knowledge, defining workflows, and configuring these commodity agents for your specific business context.

Building effective agents requires intensive, custom work for each client—data cleansing, training, and deployment by skilled engineers. Large incumbents lack the agility and cost structure to provide this bespoke service, creating an opening for focused startups who can afford the human capital.

Companies will adopt a hybrid "build vs. buy" approach. They will use AI agents to build bespoke, simple software "screwdrivers" for specific workflows on the fly, eliminating many niche SaaS tools. However, they will continue to "rent" large, foundational platforms like ERPs and CRMs, which serve as heavy-duty "trucks."

Microsoft's CTO observes that the most compelling agent-based startups are not building unique infrastructure. Instead, they are leveraging existing platforms to solve a specific user problem they understand better than anyone else. This signals a market shift from infrastructure-level to application-level innovation.

For decades, buying generalized SaaS was more efficient than building custom software. AI coding agents reverse this. Now, companies can build hyper-specific, more effective tools internally for less cost than a bloated SaaS subscription, because they only need to solve their unique problem.

As base model capabilities converge, the key differentiator is shifting to the "agent harness"—the infrastructure, tools, and skills built around the model. For vertical AI, this is where domain expertise is injected, creating specialized agents with custom tools that outperform generalist models.