Managing agent sprawl: why enterprises need an agent management platform
As enterprises accelerate AI agent deployment, a new operational risk is emerging — agent sprawl. This whitepaper maps the problem and prescribes the solution.
Reference
RX-2026-002
Written by
- LMLena Marchetti · Platform Research
In short
The hidden cost of unmanaged AI agents at enterprise scale
- Format
- PDF · 29 pages
- Published
- Feb 2026
- Access
- Free, no paywall
Why we wrote this
As enterprises accelerate AI agent deployment, a new operational risk is emerging — agent sprawl. Teams across the business are spinning up agents independently without centralised visibility, governance, or lifecycle management.
Agent sprawl is not a future concern. It is already happening. In a 2025 survey of enterprise AI adopters, 67% reported having agents deployed that their IT organisation did not fully catalogue.
This whitepaper makes the case for a dedicated Agent Management Platform as a core piece of enterprise AI infrastructure.
What’s inside
- 01What agent sprawl is, how it develops, and why traditional IT governance tools cannot contain it
- 02The five failure modes that emerge when enterprise agents operate without centralised oversight
- 03The functional requirements of an Agent Management Platform and how to evaluate vendors
- 04A 90-day roadmap for moving from ad-hoc agent deployment to governed, observable agent operations
Datasheet specification
| Reference | RX-2026-002 |
|---|---|
| Title | Managing agent sprawl: why enterprises need an agent management platform |
| Category | Agentic AI |
| Length | 29 pages |
| Published | Feb 2026 |
| Authors | Lena Marchetti |
| Access | Free download, no registration wall |
| Format | PDF, printable, screen-reader friendly |
Questions about this datasheet
What is the "Managing agent sprawl: why enterprises need an agent management platform" datasheet about?
As enterprises accelerate AI agent deployment, a new operational risk is emerging — agent sprawl. This whitepaper maps the problem and prescribes the solution. It runs to 29 pages and was published in Feb 2026.
Is the Agentic AI datasheet free to download?
Yes. It downloads as a PDF with no paywall and no registration form in the way.
Who wrote it?
Lena Marchetti, working with the ReinforcedX delivery team.
How long does it take to read?
About 46 minutes end to end. The summary and the decision checklist are where most readers start.
How soon can agentic ai work start?
Typically within a week or two of a scope being agreed. The first delivery is deliberately a small batch so you can check the output against your expectations before volume ramps.
What do you need from our team?
One process owner who knows the workflow, one engineer with access to the systems involved, and a weekly 45-minute review. No standing committee, and no requirement for an ML specialist on your side.
Who owns the output and the data?
You do. Datasets, labels, weights, evaluation suites and runbooks are yours and are handed over at the end. Your data trains your models only, with zero-retention provider settings by default.
Can you scale volume up quickly if we need it?
Yes, and the quality bar holds because the rubric and gold set are already agreed by that point. Ramping is a staffing question, not a re-scoping one, so it usually takes days rather than a new engagement.
We already have a vendor for this. Why switch?
Often you should not. The cases where teams move to us are when they cannot get a quality number out of their current vendor, or when the work is delivered as an opaque batch with no trace of how disagreements were resolved.
What happens after the engagement ends?
We stay on-call for 30 days at no extra cost, then move to an optional support retainer. Most teams also keep a quarterly evaluation review with us to catch drift early.