Runlayer, a startup offering a Model Context Protocol gateway for AI systems, sued Rippling alleging the HR software company built a clone of its product after a year-long product trial where Runlayer shared source code and roadmaps. Runlayer raised $42 million and is being represented by law firm Sullivan & Cromwell in claims of trade secret misappropriation and breach of contract. Rippling denied the allegations and confirmed it is launching its own MCP gateway, highlighting the challenge AI infrastructure startups face selling to well-resourced tech companies that can build competing products in-house.
The Agentic AI Foundation released a major update to the Model Context Protocol, an open-source standard that lets AI applications interact with external systems. The update replaces MCP's previous handshake mechanism for managing request metadata with a stateless protocol core, eliminating a single point of failure and improving scalability and recovery from outages. The protocol's authorization system is now more resistant to OAuth mix-up cyberattacks, and new extension frameworks enable custom capabilities for AI agent workflows and cybersecurity tasks.
The Model Context Protocol (MCP) released its 2026-07-28 specification, which makes MCP a stateless protocol that scales on standard HTTP infrastructure by removing session requirements and moving protocol metadata into request headers. The update introduces explicit freshness metadata (ttlMs and cacheScope) for caching, a governed extensions system, and multi-round-trip requests to replace long-lived connections, allowing any server instance to handle requests without prior session context. These changes enable MCP servers to work with ordinary load balancers and HTTP infrastructure while maintaining backward compatibility, with upgrades remaining opt-in and existing clients continuing to work unchanged.
Diagrid released Catalyst 2.0, a managed workflow engine that adds automatic failure recovery and cryptographic verification to AI agents built on 10+ frameworks including LangGraph, Microsoft Agent Framework, and Google's Agent Development Kit without requiring code changes. The platform claims up to 10 times performance improvement over open-source Dapr and enables agents to resume from the exact point of failure while cryptographically signing each step for compliance and auditability. Enterprises can now standardize on their chosen agent framework while gaining production-grade durability and verifiability that was previously unavailable across frameworks.
Diagrid released Catalyst 2.0, a durable execution layer that allows AI agents built with popular frameworks like LangGraph and OpenAI's SDK to resume from their last completed step after failure rather than starting over. The tool records model and tool call inputs and outputs across 10+ frameworks and creates a tamper-evident signed history of all agent actions using cryptographic hashing and SPIFFE identity signatures. This addresses production reliability for agents and provides compliance documentation required by regulations like the EU AI Act for high-risk deployments in financial services and healthcare.
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