How to Design an AI-Native Architecture with Salesforce Headless 360
Summary
This article walks through designing an AI-native architecture using Salesforce Headless 360, focusing on delivering real-time, personalized investment recommendations while ensuring strict data security and compliance. It demonstrates a structured approach for architects to evaluate business requirements, align technical solutions, and document design decisions, using a real-world AI startup example. Key solutions include leveraging Model Context Protocol servers for secure data access, integrating proprietary AI engines externally, and using custom named queries to enforce least privilege access. Salesforce teams can apply these insights to build scalable, secure, AI-driven systems that integrate complex external agents without sacrificing governance or performance.
Takeaways
- Paraphrase business needs into clear High-Level Requirements before designing solutions.
- Use custom MCP servers with named queries to tightly control AI agent data access.
- Leverage architectural decision records (ADR) to document and justify design choices.
- Push compliance constraints into external AI engines rather than duplicating rules in Salesforce.
- Implement layered security by restricting API permissions and using invocable actions for safe data mutations.
Imagine an AI-native startup that needs to deliver highly personalized, real-time investment recommendations while navigating complex data security and governance requirements. As the architect , you are responsible for designing a system that connects this external agent to core customer data without sacrificing performance, compliance, or scalability. This is where Salesforce Headless 360 comes in. In episode five of Think Like an Architect , we reconstruct the architectural thinking process for this scenario. Rather than focusing only on the finished architectural design, walking through this process helps strengthen the mental muscles architects use to evaluate requirements, weigh tradeoffs, and determine the right architectural approach for headless access and autonomous agents.