Dali MCP: Building Multimodal Cloud Agents for Next-Generation Infrastructure

Modern cloud infrastructure has become impossibly complex. Multi-cloud deployments, microservices architectures, and distributed systems create operational challenges that traditional management approaches cannot adequately address. Enterprises need systems that can understand, reason about, and autonomously manage this complexity.

The Shift Away from Generic Solutions

360iResearch's 2025 analysis reveals a fundamental shift: enterprises are moving away from generic AI assistants toward autonomous management systems powered by domain-tuned models. Infrastructure and DevOps teams demand specialized AI systems that understand their specific environments, tools, and operational requirements.

Generic solutions simply cannot address the complexity of modern cloud infrastructure. The demand for domain-tuned models reflects a critical insight: infrastructure management requires deep expertise. Generic AI systems trained on broad datasets cannot understand the nuances of specific infrastructure configurations, business constraints, or operational requirements.

The Architecture: Multimodal Cloud Agents

Dali MCP (Model Context Protocol) represents a new approach to infrastructure management. Rather than providing generic assistance, Dali builds multimodal cloud agents that understand infrastructure at a deep level. These agents can reason about system state, predict failures, and autonomously remediate issues—all while maintaining the auditable decision trails that enterprises require.

The multimodal nature of Dali is crucial. Infrastructure state exists across multiple dimensions: metrics, logs, configuration, network topology, and more. Traditional systems analyze these dimensions in isolation, missing the relationships and patterns that emerge when considering them together. Dali's multimodal approach creates a unified understanding that enables more intelligent decision-making.

Model Context Protocol Provides the Framework

Model Context Protocol (MCP) provides the framework for this understanding. MCP enables agents to maintain rich context about infrastructure state, historical patterns, and operational constraints. This context allows agents to make decisions that align with business requirements while maintaining system reliability and performance.

Domain-tuned models, trained specifically on infrastructure data and patterns, deliver significantly superior performance. Early deployments demonstrate substantial value: reduced mean time to resolution (MTTR), improved system reliability, and reduced operational overhead.

The Balance: Human-on-the-Loop

Autonomous management doesn't mean removing human oversight. Instead, it means shifting from human-in-the-loop to human-on-the-loop operations. Agents handle routine tasks, predict and prevent issues, and make decisions within defined parameters.

Humans provide strategic direction, handle exceptions, and maintain ultimate control. This balance enables both efficiency and safety. Agents can identify and remediate issues faster than human operators, often before they impact users. They can also optimize resource allocation, reducing costs while maintaining performance.