๐ง๐ต๐ฒ ๐๐ด๐ฒ๐ป๐ ๐ฆ๐๐ฟ๐ณ๐ฎ๐ฐ๐ฒ
Every software organization faces two hard questions. How do we connect old apps to AI? How do we build new apps that are ready for AI from day one?
Most companies try to use REST APIs for AI agents. This fails. Agents use brittle glue code to talk to REST. They have to guess what a service does. This costs money in tokens and wastes time.
A new consumer class has arrived: AI agents. They need a new way to communicate.
I propose the Agent Surface.
This is a Model Context Protocol (MCP) layer. It sits alongside REST, GraphQL, and gRPC. It is not a separate middleman. It lives inside your service.
Why do you need an Agent Surface?
- Discoverability: Agents need to know when and why to use a tool. A REST spec tells them how, but not why.
- Granularity: REST uses resources. Agents use intents. Instead of making an agent call three different endpoints to finish a task, give it one tool that does the whole job.
- Security: Agents act differently than humans. They are autonomous. You need a security model built for probabilistic callers.
- Economics: Every mistake an agent makes costs tokens. A well-designed surface reduces reasoning costs.
I suggest splitting this surface into two tiers:
- Application MCP: Business tools like quoting a policy or looking up data.
- Management MCP: Operational tools like checking health or metrics.
A customer assistant agent should only see Tier 1. An operations agent might see Tier 2. This keeps your system safe.
Stop letting AI "just figure it out." That is lazy design with a high compute bill. A curated Agent Surface encodes your business logic into tools and prompts. This makes agents faster, cheaper, and more reliable.
You do not need to rewrite your entire system. You can add this layer to existing Spring Boot or Jakarta EE services.
Build the surface today so the agents of tomorrow can work.
Source: https://dev.to/sauloos/the-agent-surface-16mh
Optional learning community: https://t.me/GyaanSetuAi