Having the fastest AI model, or an agent in every single application, means nothing if they can't talk to each other.
Takeaways
- Each time an application adds their own native AI agent, AI fragmentation worsens.
- Your most important work likely happens across many systems, but app-native AI doesn't.
- AI orchestration helps agents span multiple systems to reach the data and make the tool calls needed to be productive.
- Available tools for agents to call, and MCP availability is more important for an enterprise app than an integrated agent.
TL;DR
Most enterprises are suffering from AI fragmentation that has occurred from acquiring too many AI models, or more likely, their enterprise apps all introducing native AI systems. A fragmented AI stack fumbles cross-system work and makes AI investment irrelevant. An orchestration layer coordinates agents across systems to enable tool access and cross-work agent collaboration.
Are your AI systems unable to coordinate?
AI stack fragmentation occurs when AI systems run tasks in silos and can’t coordinate with each other. Think about how many AI systems your organization interacts with in a single day. Include in your count every AI integration introduced across every app — Microsoft, Salesforce, ServiceNow, etc. Then add whatever system your IT team may have vetted and purchased, tools people may self-service like a personal instance of ChatGPT, and then any agents built internally.
It would not be uncommon for your count to be eight systems. 10 systems. New AI integrations appear overnight in an update nobody asked for. And the result becomes an AI for everything, with nothing working cross-functionally.
Content management faced a similar problem when every department seemed to have its own way of storing records/documents and running workflows. Our messaging then was to unify silos, and the only difference today is what tools you have in your arsenal.
Why does fragmentation happen?
Fragmentation happens when app-native or point-solution apps lack complete integration protocols and a common data language.
Despite Anthropic’s MCP solving a lot of integration problems for AI to make tool calls into apps, MCP only enables systems to pass raw data between tools. They cannot pass the underlying meaning or logic to data. For a more visual analogy, MCP opens the gate to the proverbial kingdom. It does not ensure that everyone in the kingdom can understand each other.
AI language barriers include a misalignment on:
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Ontologies - Ontologies are the definitions of keywords as defined by your business and how those keywords relate to each other.
For example: Having clear definitions for the words 'customer', 'order', 'product', and 'warehouse'.
And understanding that a customer places an order, an order contains a product, and a warehouse fulfills an order.
- Semantic layers - The definitions for how business metrics are calculated and measured across databases. Without a semantic layer, separate models generate contradictory reports from the same data.
- Contextual memory - Prompts entered and work completed by an AI system running in Salesforce aren't remembered by your instance of Copilot running in SharePoint. The workaround becomes a lot of repeated prompts and copy/pasted results from one system to the other.
Fragmentation creates fatigue and risk
We’ve already covered an experience that anyone who uses AI likely shares — micromanaging, multiple systems simultaneously, copy/pasting results from one platform to another, and repeating work only to get different outputs.
Outputs that carry different definitions and interpretations of your business are unreliable, untrustworthy, and wasted time. Which is why all those point-solution agents can create more work than they remove, and become exhaustive for end-users to juggle tools with no reliable way to run results across the business.
There’s also the matter of governance. The system IT has approved enforces data security and governance standards. But the GitHub agent being fed the output from the governed agent likely doesn’t. When those systems cannot share context or coordinate actions, an employee’s hopefully good judgment becomes the last line of defense for data and AI governance.
Wrangle your fragmented AI with orchestration
- 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.
Solving fragmentation with orchestration
AI orchestration enables AI systems to exchange context, call tools, share results, and complete work across the enterprise in a coordination hub. AI orchestration solves integration challenges by effectively marrying MCP for agent-to-tool communication and A2A (agent-to-agent).
AI orchestration collects the desired and/or required AI systems and organizes them like a dispatch team from one centralized hub. When a human prompts the orchestrator agent, that agent delineates the task into smaller tasks. One agent may go pull live customer data from a CRM and interact with the point solution’s internal AI, while another makes an external tool call. Results are then returned to the unified hub where the persistent ontology, semantics, governance, and contextual understanding are enforced.
As what is essentially a traffic controller for enterprise agentic workflows, orchestration expands the value of individual models without limiting the system to a single platform. The quality of your models still does bear some significance, but even the fastest or smartest models aren’t worth the investment if you can’t utilize them to operate in multiple places where work gets done.
With orchestration, the AI stack operates as a single system rather than something fragmented, while the orchestration layer manages context, handoffs, permissions, and the rules to govern how work moves across the business.
Fragmented AI describes AI systems operating in point solutions that are unable to communicate or collaborate with one another to complete goals across the enterprise.
AI systems become fragmented when they can’t communicate ontologies, contextual, or semantic understanding outside of their siloed app which is the case for many app native AI-integrations.
Ontologies are keywords in your business as defined by your business. AI agents need to understand what the word ‘order’ or ‘customer’ means to your business in order to produce reliable and relevant outputs.
Semantic data is the actual information of your business assigned by your business. AI agents need to understand that if an ontology defines ‘customer’ as a three digit number, then ID# 982 is a customer.
Contextual data provides AI agents with information about the current situation to improve the accuracy and quality of responses. If a user is requesting information, knowing their relationship to the organization, what device are they using, or where they are asking from can help generate more relevant, or more secure response.
AI orchestration opens agents to MCP and A2A protocols allowing specialized and multi-modal agents to coordinate across apps and solutions.
