A key bottleneck preventing AI agents from performing meaningful tasks is the lack of secure access to user credentials. Companies like 1Password are building a foundational "trust layer" that allows users to authorize agents on-demand while maintaining end-to-end encryption. This secure credentialing infrastructure is a critical unlock for the entire agentic AI economy.

Building a functional AI agent demo is now straightforward. However, the true challenge lies in the final stage: making it secure, reliable, and scalable for enterprise use. This is the 'last mile' where the majority of projects falter due to unforeseen complexity in security, observability, and reliability.

WorkOS CEO Michael Grinich observes that AI products inherently touch sensitive corporate data, forcing them to become 'enterprise-ready' in their first or second year. This is a much faster timeline than traditional SaaS companies, which often took over five years to move upmarket.

Established SaaS companies can defend against AI disruption by leaning into their role as secure, compliant systems of record. While AI can replicate features, it cannot easily replace the years of trust, security protocols, and enterprise-grade support that large companies pay for. Their value shifts from UI to being a trusted database.

In large enterprises, AI adoption creates a conflict. The CTO pushes for speed and innovation via AI agents, while the CISO worries about security risks from a flood of AI-generated code. Successful devtools must address this duality, providing developer leverage while ensuring security for the CISO.

Unlike past tech waves where security was a trade-off against speed, with AI it's the foundation of adoption. If users don't trust an AI system to be safe and secure, they won't use it, rendering it unproductive by default. Therefore, trust enables productivity.

Vercel is building infrastructure based on a threat model where developers cannot be trusted to handle security correctly. By extracting critical functions like authentication and data access from the application code, the platform can enforce security regardless of the quality or origin (human or AI) of the app's code.

The CEO of WorkOS describes AI agents as 'crazy hyperactive interns' that can access all systems and wreak havoc at machine speed. This makes agent-specific security—focusing on authentication, permissions, and safeguards against prompt injection—a massive and urgent challenge for the industry.

Instead of pure SaaS, Terra Security uses an "AI-enabled service" model. This hybrid approach allows them to tackle complex problems that fully autonomous AI can't yet solve, while still benefiting from software scalability and replacing existing, large budget items for manual services.