Multi-Agent Collaboration

Shared Context for Collaborative Agent Workflows

Today’s agents operate in silos and can't coordinate effectively. With OCL, a user can train their own personal agent on a specific context. This agent acts as the liaison between all external agentic communications, essentially acting as the mediator for handling coordination and enabling execution of complex tasks.

In future releases, multi-agent permissioned systems can also be created using Agent-to-Agent (A2A) protocols, so that multiple agents have access to the same context and can collaborate based on the same data.

Developer Use Case: Portable Prompt + Memory Stack

Devs build custom agents but can't move memory between models or providers.

Using OCL, developers plug into a universal context API to:

• Build AI agents with swappable LLMs (Claude, GPT, open source)

• Retain consistent memory across model updates or provider changes

This unlocks better agent interoperability and reduces vendor lock-in.