AI adoption is exploding across organizations, and with it, a new kind of chaos is emerging. Employees are spinning up their own ChatGPT agents, Gemini workflows, and home-grown AI assistants powered by Claude, Llama, and internal vector databases. Every team has their own bot. Every department has its own RAG pipeline. Sensitive documents get uploaded. Agents get published. And suddenly, the enterprise is drowning in AI sprawl.
CIOs and IT leaders now have a new operational mandate: bring order, governance, and visibility to a rapidly multiplying AI landscape without slowing innovation.
This article breaks down the risks, the architectural patterns that solve them, and the commercial tools already emerging to help.
The Risk: Uncontrolled Agent Proliferation
As organizations let employees create custom AI agents, three predictable problems appear:
1. Shadow Data Pipelines
Anyone can upload internal documents into retrieval augmented generation (RAG) systems. Those sensitive documents are summarized as “knowledge” which becomes embedded into the agent. When the agent is published, that potentially sensitive knowledge becomes visible to anyone who prompts it.
2. No Agent Discovery or Ownership
I see companies deploying AI the way we deployed SaaS 10-12 years ago, which I honestly believe is doomed to fail. Each siloed team deploys its own solution, advertises it internally, and horizontal adoption is achieved organically. But here’s where AI differs from SaaS: modern AI solutions make it incredibly easy to build citizen-developed solutions. Citizen development is generally a good thing. But in a city where anyone can build a road or a skyscraper, some overarching governance is required.
User experience is also challenging in this environment. The process of “finding the right agent” becomes akin to searching a massive book library, one catalog card at a time. Multiple versions of the same HR bot, the same procurement assistant, or the same engineering bot float around in isolation. There’s no registry, no app store, and worst of all – no centralized ownership.
3. No Agent and Data Governance Layer
Let’s be real for a minute: AI is challenging data borders, eroding traditional RBAC mechanisms, and moves data en masse into highly abstract systems where average users make discretionary access decisions they often don’t understand. I see companies spend millions on data loss prevention solutions, identity protection systems, and countless other security tools; only to move corporate data into AI black boxes very few people truly understand.
The root cause of the problem is that data classification isn’t enforced, and role-based access from source systems isn’t honored in the destination AI solution. We take data from source systems, where access control is generally fine-grained, then bulldoze that information into an AI agent where access is often binary: private or public. Data sources are rarely reviewed. Embeddings are created with no controls. Sensitive outputs are returned with no redaction or DLP guardrails.
Without intervention, this creates compliance exposure, reputational risk, duplicated effort, and inconsistent output quality.
The Fix: Build an Enterprise AI Control Plane
Solving AI sprawl isn’t about restricting AI. It’s about operationalizing it at scale. Successful enterprises are taking a three-pronged architectural approach shown below.
1. Centralized AI Gateway (Enforced Policy Layer)
All agent executions and model calls — including ChatGPT, Gemini, Claude, and internal LLMs — flow through a unified policy proxy.
This gateway enforces:
- SSO + attribute-based access control
- Pre-ingest classification for RAG documents
- Redaction and DLP on outputs
- Scoped context retrieval (only approved vector indexes)
- Logging and auditability tied to the user and the agent
This is your “API firewall for LLMs.”
2. AI Agent Registry (CMDB for Agents)
Every agent becomes a governed asset with metadata, versioning, ownership, and searchable descriptions. Think of it as a Service Catalog for AI.
A minimal schema includes:
- Owner and business purpose
- Supported model(s)
- RAG sources and sensitivity levels
- Approval/review state
- Lifecycle status (test, published, deprecated)
- Usage metrics
This becomes the source of truth for visibility, compliance, and reuse.
3. A Meta-Agent (“Agent of Agents”)
This is a single assistant connected to the registry that can:
- Answer “What agents exist for benefits enrollment?”
- Route a user to the correct bot instead of creating a new one
- Summarize capabilities and ownership
- Provide guidance before new agents are created
- Keep the CMDB updated so humans don’t have to
This dramatically reduces duplication and discovery challenges.
Real-World Platforms Entering the Space
The beginnings of this “CMDB for AI agents” category are already forming. Several vendors are addressing pieces of the problem:
Workday Agent Governance
Workday now offers a unified console to “register, monitor, and govern all your agents,” including those created outside Workday’s ecosystem. Useful for enterprises already on the Workday platform.
Collibra AI Governance
Collibra allows organizations to register AI agents as assets, link them to data sets, and apply policy controls. Strong alignment for enterprises already using Collibra for data cataloging.
Emergence AI
Built around a full agent ecosystem, Emergence includes an Agent Registry, tool registries, and data registries; essentially treating the enterprise as a network of agents, tools, and datasets.
TeamForm
Provides a “system-of-record” that maps people, AI agents, and work into a single operational graph. Useful when you need to align agents with org structure and business outcomes.
cheqd Trust Registries
Focuses on agent identity, accreditation, and verification — solving the trust and authenticity component of governance.
While each platform covers different governance domains, the important point is this: the market is validating the need for an agent registry layer.
How CIOs Can Get Ahead of AI Sprawl Today
Whether or not a commercial solution is deployed, organizations can take immediate steps:
1. Require all agents to be registered before publication.
2. Enforce a central AI gateway for any model access or RAG indexing.
3. Implement data classification checks before anything is embedded.
4. Attach ownership, business purpose, and data lineage to each agent.
5. Deploy a meta-agent to help users find the right tool before creating a new one.
6. Introduce lifecycle controls (approval, deprecation, versioning).
7. Make usage visible so the organization knows which agents matter and which can be retired.
The Strategic Payoff
Enterprises that get ahead of AI sprawl don’t just reduce risk, they unlock efficiency:
- Fewer redundant agents
- Higher quality outputs
- Better policy compliance
- Faster onboarding to AI tools
- Increased trust from business users
- And a more scalable, more governable AI ecosystem
AI is not slowing down. The organizations that thrive will be the ones that build an intentional layer of governance, discoverability, and operational discipline around it.