AI Agent Sprawl · Use case
AI agent sprawl: turn scattered agents into one governed, owned AI estate
Every team builds agents on its own platform. Nobody sees the whole picture. Kindo, the AI agent management platform, gives IT, Security, Finance, and Compliance one registry, one policy, and one record for every agent, wherever it was built.
ANY BUILDER
ONE REGISTRY
AN OWNER PER AGENT
ONE RECORD
AIR-GAPPED SUPPORTED
Agents built anywhere
Office assistants
Developer harnesses
Internal builders
AI inside SaaS
Kindo
One place to reach models and data
Registry
Owner and permissions
Data gateway
Model gateway
One record
IT
Security
Finance
Compliance
What is AI agent sprawl?
AI agent sprawl is the uncontrolled growth of AI agents across an enterprise, built on different platforms by different teams, with no shared inventory, owner, policy, or record. The AI estate is every agent, model, and tool; sprawl is the estate without management.
No inventory
Nobody holds the list
No owner
Nobody on the hook
No policy
Access is whatever the builder had
No record
What it did, what it cost: unknown
A subset
Shadow AI
The agents you don't know about. Shadow AI Discovery finds them.
The previous era
SaaS sprawl
An app waits for a person. An agent reads data and acts.
This page
AI agent sprawl
Approved agents too. Approval says someone said yes, not who owns it now or what it did last night. What an AI agent management platform manages.
Why does AI agent sprawl matter?
The agents multiply whether or not anyone is counting, and at the scale Gartner projects, sprawl cannot be managed by hand. Governance arrives before the first incident, or after it.
<15 → 150,000+
Agents at the average global Fortune 500 company, 2025 to 2028, in Gartner's projection.
50%
of agent-to-agent interactions guardrailed by AI agent management platforms by 2030. Gartner projection.
~1 in 3
Ten agent calls at 10% error each: the task completes about a third of the time. Errors compound. So does unowned risk.
~60%
of organizations report evidence of, or suspect, unauthorized AI agent use. Gartner survey.
Sources: Gartner, Market Overview: AI Agent Management Platforms, July 2026; Gartner, Beyond Agent Sprawl, March 2026; Gartner, Use Guardian Agents to Govern Autonomous AI Systems, September 2026. Projections are Gartner's.
What problems does Kindo solve?
Kindo answers the four questions a CIO or CISO cannot answer across builders today, from one place. The builders stay.
Inventory
One inventory of every agent, whatever built it?
Each builder keeps its own list. Nobody holds the union.
Kindo → Pre-built discovery agents read where agents leave traces. Unified AI Registry: every agent, where it runs, who owns it, what it reaches.
SIEM, identity provider, expense system stay the sources.
Ownership
An owner for every agent?
Agents outlive projects, contractors, reorgs.
Kindo → Every agent tied to a person and a Group from Okta, Entra, or LDAP. No owner, no keys.
Your identity provider stays the source of truth.
Policy
One access policy across platforms?
Each builder enforces its own rules. Or none.
Kindo → Agents reach models and data through Kindo's gateways. One policy engine, one DLP set, whatever the harness.
Your AI gateway can sit underneath as a provider.
Autonomy
Teams keep building, control stays central?
Blocking agents moves the cost: tickets, gaps, spend elsewhere.
Kindo → Build anywhere. Kindo issues the keys, meters the cost, keeps the record. Yes stays safe.
No rebuild.
The CISO's fifth question: who is accountable when an autonomous agent does something destructive? On the record before it acts: owner, Group, policy, and, in Kindo's harness, the person who approved the step.
How does Kindo deliver AI agent governance across every builder?
Kindo becomes the place every agent reaches models and data, ties each agent to an owner and a policy, and keeps one record. Four steps for any agent. A fifth for agents in Kindo's harness.
1
Discover and register
Pre-built discovery agents read your sources. Output: the Unified AI Registry. This is the free assessment.
2
Assign an owner and a Group
From Okta, Microsoft Entra, or LDAP. Every agent inherits its operator's permissions.
3
Route model and data calls through Kindo
Kindo issues the keys. Policy and DLP apply, whatever the harness.
4
Record and attribute
Every call in the Audit Trail. Every token to an agent, owner, and team. Budget alerts.
5
Govern the run · agents in Kindo's harness
Step-level approval gates, pause and resume, per-run credentials.
Any agent · via the gateways
Registry entry
Named owner
Inherited permissions
Policy and DLP
Cost attribution
Budget alerts
Call-level record
Revoke or block
Agents in Kindo's harness · everything left, plus
Step-level approval
Pause and resume mid-run
Agent-to-agent inspection
Full action record
No rebuild. Agents stay where they were built; your existing gateway stays, as a provider inside Kindo.
RUNS: KINDO CLOUD · YOUR CLOUD · ON PREMISES · AIR-GAPPED
KEYS ISSUED AND REVOKED BY KINDO
Gartner: every agent is a digital employee
Job description
→ Unified AI Registry
Verified owner, assigned rights
→ Identity and Access Control
Performance record
→ Audit Trail
The mapping is Kindo's view.
Source: Gartner, Use Guardian Agents to Govern Autonomous AI Systems, September 2026.
What will you gain?
A governed AI estate: every agent known, owned, permitted, measured, and retirable, from one place. An estate you can report on.
Known
In the Unified AI Registry
Owned
A named person or Group
Permitted
Access by policy
Measured
Cost and actions attributed
Retirable
Keys revoked from one place
One record
Four teams, four answers
Every agent action, cost, and policy decision, read by all four.
IT
Every agent, owner, and state
One registry across builders. 24x7 support for agents run in Kindo's harness.
Security
What each agent can touch
Policy and DLP at the gateway. Revoke a key or block a model from one place.
Finance
Cost by agent, owner, team
Every token attributed. Budget alerts at the threshold you set.
Compliance
One audit trail across builders
Human and AI actions, one record. Maps to NIST AI RMF and EU AI Act oversight expectations.
Who is AI agent sprawl control for?
The leaders who said yes to agents and need to keep saying it: the CIO and CTO, the CISO, and the central AI platform leader consolidating builders.
CIO and CTO
Say yes without losing the plot
One view across builders. No lock-in to one lab or builder.
CISO
Answer "what can this thing touch"
Inherited permissions. Policy and DLP at the gateway. Every call on record.
AI platform leader
Consolidate without rip and replace
Keep your gateway or model hub. Kindo fronts it and issues the keys harnesses use.
Agents on more than one platform (a suite copilot, a developer harness, an internal builder is the common case)
Agents that read company data or take actions, not only answer questions
Leadership asking what exists, who owns it, what it costs
Why choose Kindo for AI agent sprawl?
Kindo manages agents from anywhere rather than building them in one place, with the three capabilities control needs together: AI Data Gateway, AI Model Gateway, and Role Based Access Control, deployable on premises. Two differences, one objection, and where Gartner stands.
Difference 1
Manage agents from anywhere, not build them in one place
By category. Your gateway and builders keep their jobs.
Approach
Agents governed
Owner per agent
Runtime enforcement
Cost attribution
Record
Runs
Agent development platform
Its own
Its own only
Its own only
Its own usage
Its own log
Vendor cloud
AI gateway
Traffic through it
No; keys, not owners
Model calls only
By key
Call log
Varies
AI governance platform
Declared agents
As declared
Catalog, little runtime
Rarely
Attestations
Vendor cloud
Kindo
Built anywhere, via the gateways
Yes, from your identity provider
Data and model calls; policy, DLP, budget alerts
Every token: agent, owner, team
One Audit Trail across builders
Kindo cloud, yours, on premises, air-gapped
Difference 2
Every agent's activity lands in one record, read by four teams
Not four tool logs.
From any agent
A model or data call
agent · owner · Group · model · source · tokens
One record in Kindo
Read by
One history, not four tool logs
Kindo cloud · your cloud · on premises · air-gapped
The objection
"We already have an AI gateway"
Keep it. Row by row, what Kindo adds in front.
A gateway alone
What Kindo adds in front
Sees only traffic routed through it. Agents elsewhere: invisible.
The Unified AI Registry spans agents not routed yet, and names them.
Knows keys, not people. No owner, Group, or inherited permissions.
RBAC ties every agent to an owner and a Group from your identity provider.
Model calls only. Data reads and writes go around it.
The AI Data Gateway puts policy and DLP on every data read and write.
Rarely on premises with identity and data control together.
Keep it as a provider inside Kindo. DLP, policy, cost, audit in front. Any deployment.
Gartner's view
Agent sprawl is a problem AI agent management platforms exist to address
Gartner names mitigating agent sprawl as one of the three challenges AI agent management platforms address. Mandatory features it lists: unified registry, agent FinOps, guardrail management, integration management, life cycle management. Its definition: agents managed independent of where they are deployed, one interface for cost and ROI, one view of governance and security controls.
Kindo's view: Unified AI Registry, Agent FinOps, policy engine, gateways, and the five-stage agent lifecycle map to those features.
Source: Gartner, Market Overview: AI Agent Management Platforms, July 2026.
How do you get started?
Start with the free assessment: Kindo's pre-built discovery agents build the registry everything else depends on, from sources you already have. Then a demo of how Kindo maps to your agent estate.
30 days · three sessions · an Executive Summary and a Risk Assessment. Discovery only; governing the estate is the platform, scoped separately.
Go deeper on managing the AI estate
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Platform
The Enterprise AI Control Center
Three core capabilities. How any agent connects.
Use case
Shadow AI Discovery
The part of the estate you don't know about yet.
Use case
AI Cost Control
Every token to an agent, owner, and team.
Frequently asked questions about AI agent sprawl
What is an AI agent management platform?
Software that manages AI agents independent of where they were built or deployed: one view of cost, one view of governance and security controls. Kindo is one, also called an enterprise AI control center, built for the teams that support and control agents, not for the developers who build them.
How is AI agent sprawl different from shadow AI?
Shadow AI is the AI you don't know about. Sprawl includes it, plus every approved agent with no current owner, policy, or record. The free assessment finds both: it builds the registry from your sources, not from what people declared.
Do we have to rebuild agents in Kindo to govern them?
No. Third-party agents stay where they are, pointed at Kindo's AI Model Gateway and AI Data Gateway with keys Kindo issues: governed access and a call-level record. Step-level gates, pause and resume, and the full action record apply to agents in Kindo's harness or ported in. Agent Builder and Action Chat are optional.
How does an agent inherit permissions?
Users and Groups import from Okta, Microsoft Entra, or LDAP into Kindo's Role Based Access Control. Each agent is tied to the person or Group that operates it and reaches only what they may reach. Admins add per-model and per-source rules and DLP filters on top.
Can Kindo govern agents built in Copilot Studio?
Through Azure AI Foundry. Kindo appears there as a model family; Copilot Studio agents select it, and their model traffic passes through Kindo's gateways. Copilot Studio exposes no model API of its own, so that routing is the control point today. Roughly 80% of enterprise workloads are coverable this way now; the rest follow as vendors open APIs.
What is the difference between an agent registry and an AI governance platform?
A governance platform catalogs declared agents and policies and produces attestations. A discovered registry lists what is actually running, declared or not. Kindo builds the registry from your sources, then enforces at runtime through its gateways: catalog and enforcement in one system.
How does Kindo handle agent-to-agent calls?
Every agent's model and data calls pass through the gateways, so each hop is policed and recorded against its agent and owner. Inspection of the messages agents pass each other applies to agents in Kindo's harness.
Can the free assessment block agents?
No. The free assessment is discovery: it builds the registry and returns an Executive Summary and a Risk Assessment. Enforcement and budget alerts are part of the platform, scoped separately once the registry exists.
What happens after the 30 days?
You keep the findings. Going further: the connected sources become the AI Data Gateway, discovered agents are pointed at Kindo's gateways, and users import into RBAC from your identity provider. Most of the work of standing up an agent management platform, already done.
