The gap between a working AI prototype and a production-grade system is enormous. In our experience deploying agentic AI across Fortune 500 enterprises, the prototype typically represents about 10% of the total effort. The remaining 90% involves reliability engineering, error handling, observability, and integrating with existing enterprise systems.
Agentic AI systems differ fundamentally from traditional AI pipelines. Instead of a single model call, you're orchestrating multiple agents that make decisions, take actions, and coordinate with each other. This introduces complexity around state management, error recovery, and human-in-the-loop workflows that most teams underestimate.
At Moring AI, we've developed a platform-first approach that addresses these challenges. Our Agentic AI Platform provides the infrastructure primitives — RAG pipelines, model gateways, agent observability, and MCP servers — that let teams focus on business logic rather than plumbing.
The key insight is that production AI systems need the same rigor as production software systems. You need CI/CD for model deployments, observability for agent behavior, cost controls for inference spending, and security guardrails for data access. Our platform bakes these in from day one.
Forward-deployed engineering is our secret weapon. Instead of handing off a platform and hoping for the best, our engineers embed directly with customer teams. They bring deep AI expertise combined with the ability to understand and navigate complex enterprise environments.
The result is dramatically faster time-to-value. What typically takes enterprises 12-18 months to build internally, we deliver in 8-12 weeks. And because our platform is composable, each new use case builds on the infrastructure from previous ones.