Standardizing the Future of Agents with MCP
An open standard for connecting AI assistants to systems. Dali MCP leverages this protocol to ensure safe, reliable communication between infrastructure agents and cloud provider APIs.
Read Announcement →Next-generation multimodal cloud agents for scalable infrastructure automation.
Dali MCP introduces a new layer of abstraction for cloud infrastructure: the Agentic Control Plane. Instead of static configuration files (IaC), Dali uses multimodal agents that understand architectural diagrams, documentation, and intent.
These agents autonomously plan, provision, and optimize resources across multi-cloud environments, adhering to the Model Context Protocol (MCP) for standardized reasoning.
Traditional auto-scaling is reactive. Dali's predictive agents analyze usage patterns and potential failure points to preemptively adjust infrastructure.
When anomalies occur, Dali autonomously investigates logs, identifies root causes, and executes remediation strategies—slashing mean time to recovery (MTTR).
Reduction in Mean Time to Recovery for infrastructure incidents managed by autonomous agents compared to human-led ops teams.
Average reduction in monthly cloud spend through Dali's predictive resource optimization and waste elimination.
Continuous autonomous oversight of complex multi-region deployments, ensuring 99.999% uptime SLAs are met without human burnout.
An open standard for connecting AI assistants to systems. Dali MCP leverages this protocol to ensure safe, reliable communication between infrastructure agents and cloud provider APIs.
Read Announcement →Stanford research paper detailing strategies for cascading model usage and resource allocation—principles we embed directly into Dali's optimization logic to minimize inference and compute costs.
Read on arXiv →