Problem solved

AI agents built with different frameworks lack a common way to discover and securely call each other's tools across networks. Agenium solves this by providing a DNS-like resolution mechanism for agents, where each agent registers under a human-readable address (e.g., agent://weather). Other agents can then discover and invoke its tools using standard URIs, with built-in handshake and trust verification, eliminating the need for hardcoded endpoints or custom service discovery.

Use cases

• Registering an MCP-enabled weather tool as an agent on the agent:// network so other agents can query it via agent://weather/tools/current.
• Building a distributed monitoring system where an API health agent (agent://api-health) continuously checks endpoints and another agent queries its status.
• Creating a webhook relay agent that bridges external HTTP webhooks (e.g., GitHub, Stripe) into the agent ecosystem, allowing other agents to subscribe to events.
• Setting up a fleet of specialized agents (echo, translator, helper) that can be dynamically discovered and composed to handle multi-step tasks.
• Enabling an AI assistant to dynamically find and use third-party tools without prior configuration, simply by resolving agent:// URIs.

Audience

• Developers
• ML Engineers
• Researchers

Pros

• Enables decentralized discovery of agents via intuitive URIs.
• Provides cryptographic handshake and identity verification for secure inter-agent communication.
• Seamless integration with existing MCP servers, extending their reach.
• Includes practical blueprints and demo agents for quick adoption.
• Open-source and self-hostable with Docker support.

Cons

• Very early stage with minimal community adoption (only 2 GitHub stars).
• Requires running and maintaining a dedicated DNS server for agent resolution.
• Limited to MCP-based tools; may not suit agents using other protocols.