Blocks.ai
The network layer for AI agents, enabling them to run anywhere and be called globally
Blocks.ai solves a highly technical yet practical problem: once you've built an AI agent running on your laptop, a cloud server, or behind a corporate firewall, how do others actually call it? It provides a networking and control plane where an agent only needs a single outbound connection to be accessed by applications worldwide.
Key Features
- Deploy and expose agents with a single outbound connection
- Built-in authentication and routing
- Support for real-time requests and continuous streaming
- Agent marketplace and discovery mechanisms
- Built-in billing and revenue sharing
Pros
- Low deployment barrier; runs seamlessly behind firewalls
- Built-in billing allows developers to monetize directly
- Supports streaming workloads
Cons
- Ecosystem is still in its early stages with a limited selection of agents
- 15% revenue share requires cost calculations for high-traffic scenarios
- Connecting agents to a third-party network requires security evaluations
Use Cases
- Exposing personal agents to provide external services
- Opening internal enterprise agents to partners
- Integrating ready-made agent capabilities into applications
- Monetizing agent services
Editor's Note
What AI agents lack most right now isn't capability—it's how to be discovered, how to be called, and how to get paid. Infrastructure is always more boring than a demo, but it's what determines whether the ecosystem can actually grow.
FAQ
Does data pass through the platform?
As a networking layer, requests and responses are routed through it. Always verify encryption methods and data retention policies before handling sensitive data, and evaluate carefully before connecting internal, highly sensitive systems.
How is this different from setting up my own API?
Self-hosting requires managing domains, SSL certificates, authentication, billing, and availability. This platform bundles all those chores away, with the trade-off of platform lock-in and revenue sharing. It's cost-effective for small-scale validation, but high-volume workloads require a cost re-evaluation.
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