AI orchestration enables disparate AI systems to collaborate across applications and interfaces — automating data streams and task execution with human oversight.
Takeaways
- AI orchestration is the coordination and management of AI models, agents, data flows, and integrations to share understanding and complete work across the enterprise.
- Orchestration blends MCP and A2A, allowing for API connectivity and shared contextual understanding.
- AI agents continue to scale rapidly with enterprises experiencing fragmented AI through internal builds or unexpected app updates.
- Teams can build a custom orchestration layer, find one managed alongside current content management capabilities, or utilize a purpose-built platform.
TL;DR
AI orchestration coordinates AI models, agents, tools, and integrations to coordinate collaborative work across the enterprise. Enterprises orchestrating AI generate more value from AI models that synchronize functions and share contextual understanding of the larger tasks.
Tuning the orchestra
In the musical sense, an orchestra is a group of musicians led by a conductor who organizes the many players to perform one cohesive piece. You can think of enterprise AI the same way — where the agents are the musicians, the models they run are the instruments they play, and the individual tasks they must complete are the individualized lines of music they must play.
Uncoordinated, non-orchestrated AI is the equivalent of having these musicians try to perform one piece while wearing noise-canceling headphones and without the guidance of a single conductor at the front to communicate timing, cues, and the needed volume of each individual instrument. It doesn’t matter just how good each individual musician is — without being able to tell where they are in relation to the music and the other musicians; the result will almost certainly sound chaotic.
What does it mean to orchestrate AI?
Orchestrating AI means managing complex AI workflows in a manner where AI systems and agents have a complete picture of the task at hand, the role they play, and the tasks/roles of all other agents, systems, and integrations involved.
AI orchestration solves the problem of fragmented AI by enabling AI systems present in just about every app interface to swap information with agents outside of their native solution. This is made possible through a mixture of MCP and A2A protocols (explained further below).
What other AI workflow conundrums does orchestration solve?
- Which agent or model handles which task, and in what sequence?
- What context and data does each agent workflow step receive, and from where?
- Who or what has authority to approve, override, or stop an agent action?
- What agent actions and reasoning get logged for human auditability?
- What happens when a step fails, times out, or returns garbage?
What is the model context protocol? (MCP)
MCP is a standardized way of connecting AI models to external applications, enabling AI systems to make tool calls to acquire and interact with data in apps – increasing model performance and reducing hallucinations.
What is A2A protocol? (Agent2Agent)
A2A is a standard for connecting agents to other agents, enabling agents to exchange, communicate requests, and complete tasks on each other’s behalf.
Orchestration is the missing layer for AI
- AI orchestration enables AI agents and systems to communicate across platforms.
- AI orchestration is the difference between working with AI, and AI that works for you.
- Orchestrated agents transform data-in-motion.
Definitions defined
What is the model context protocol? (MCP)
MCP is a standardized way of connecting AI models to external applications, enabling AI systems to make tool calls to acquire and interact with data in apps – increasing model performance and reducing hallucinations.
What is A2A protocol? (Agent2Agent)
A2A is a standard for connecting agents to other agents, enabling agents to exchange, communicate requests, and complete tasks on each other’s behalf.
Why is orchestration becoming non-negotiable?
A study conducted by IBM Institute for Business Value reports continuous scaling of AI systems, with surveyed respondents reporting a 38% growth of newly deployed agents in the coming year. On top of AI integrations appearing overnight in app updates, individuals and departments are buying/building their own agents to get more done. The problem, as discussed in our post on fragmented AI, is that these new agents end up creating more siloes and more work than they intend to fix because none of them work together.
Setting-up an orchestration layer
With over 10,000 active public MCP servers, many platforms have already streamlined how enterprise tools and data are made available to AI . The next step is transitioning from single-instance tool calls to a centralized environment where an AI conductor can coordinate available agents, delegate tasks, and govern agentic behavior. This centralized governing platform can be built with custom development; integrated into existing database networks and content-driven systems; or exist as part of a ready-made orchestration-specific platform. Let’s take a look at each.
- Custom development: Your team builds the orchestration layer in-house, wiring models, connections, and logic. Custom development obviously affords the most customization but at the cost of significant time and resource investments. We typically wouldn't recommend this route without very strong internal resources for deployment and long-term maintenance.
- Content-centric platform: Modernizing enterprise content platforms are adding agent orchestration as an important layer on top of existing document structures, governance policies, retention rules, and access controls. If you're in a regulated or document-heavy industry and you already trust the platform managing content, this is a relatively straightforward path to orchestration.
- No-code pre-built platform: Modern platforms like Vertesia (interviewed on The Mostly Unstructured Podcast above) are entering the market, built with agentic workflows in mind. These intuitive all-in-one platforms plug into where your content and business systems live, offering the advantage of multimodal orchestration without vendor lock-in.
FAQ
AI orchestration is the coordination of multiple AI models, agents, and data sources so they work together as one governed system. It manages sequencing, context sharing, error handling, and oversight across an AI workflow.
An agent is a system built around a model to perform tasks. Orchestration sits above agents, coordinating how multiple agents, models, and tools interact, share data, and hand off work.
Uncoordinated AI deployment creates sprawl, duplicated effort, and governance blind spots. Orchestration restores visibility and control, which is why it matters as agent counts climb and accountability lands on technology leaders.
No. Traditional automation follows rigid, predefined rules. Orchestration coordinates systems that reason and act with autonomy, managing their sequencing, context, and governance rather than scripting every step.
