How or securely plug AI agents to core systems

💡 How can enterprises safely and effectively adapt their architectures for AI agent interactions?

We've tackled similar challenges before. When web and mobile apps emerged, enterprises needed safe, scalable access to backend systems without compromising security or core capabilities. The answer? Backend for Frontend (BFF), an architectural pattern that safely abstracted legacy systems behind client-specific APIs.

Now, we're facing a similar inflection point with AI agents. Traditional APIs aren't optimized for agent interactions—they lack:

🔍 Semantic clarity: Explicitly defined actions understandable to AI agents.
🧠 Contextual awareness: Ability to convey relevant situational context clearly.
🧩 Task orientation: Designed specifically around agent-driven workflows.

Maybe it's time for a new pattern—Backend for Agent (BFA)—providing:

🔐 Security and governance specifically tailored for AI agent interactions.
💡 A semantic layer clearly describing actions and data in an AI-friendly manner.
🧩 Contextual abstraction over generic APIs, enabling smoother, more meaningful agent interactions.

The BFF model enabled safe modernization. Could BFA similarly guide responsible AI agent enablement?

💬 I'd love your thoughts: Is BFA the next architectural evolution? Have you started thinking about agent-specific integrations in your organization?