Organizations today rarely operate a single AI model in isolation. Most run dozens, occasionally hundreds, each assigned to a narrow function like summarizing documents, routing customer inquiries, retrieving data from internal systems.
According to Salesforce's 2026 Connectivity Report, organizations now run an average of 12 AI agents, a figure expected to grow 67% within two years. Yet half of these agents currently operate in isolated silos, disconnected from one another and from the broader business. Individually, these tools perform well. Collectively, they frequently fail to communicate with one another, and this disconnect is precisely the problem orchestration is designed to solve.
At its core, AI orchestration concerns sequencing and coordination. When a task moves through multiple models or agents, a governing layer must determine execution order, manage handoffs between systems, and flag outputs requiring review. This orchestration layer, and the framework supporting it, ultimately determines whether a multi-agent system performs predictably or breaks down under real-world complexity.
This challenge is particularly evident in AI agent orchestration, where autonomous agents are expected to transfer work between one another with minimal human intervention. Executing these handoffs reliably at scale remains one of the more difficult aspects of production AI systems and a common point of failure. Professionals looking to build expertise in this area can upskill through USAII®'s CAIE™ Certification program, which establishes the foundational RAG pipeline and agent integration skills that orchestration concepts build directly upon.
Download the Guide to explore how AI orchestration works.
