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ZDNET's key takeaways

• The number of agents continues to grow, increasing the risk of sprawl.
• Professionals must consider using agent management systems.
• These systems can help manage agent sprawl, but beware of challenges.

Enterprises worldwide have 28.6 million active agents, a figure forecast to exceed 2.2 billion by 2030, according to Statista.

Also: These top 30 AI agents deliver a mix of functions and autonomy

Agent wranglers are required to bring management sensibilities to this growing space. So, can AI agent sprawl be tamed? Some vendors are giving it a try, leading to a new technology category, agent management systems, that are tasked with managing networks of AI agents. 

Building a platform

An agent management platform essentially acts as a digital HR department for AI agents, and experts suggest now is the right time for such offerings. 

Agents running outside of management frameworks are essentially the AI equivalent of shadow IT. 

"It works until it doesn't, and when it stops working, you have no audit trail, no version control, and no governance to fall back on," noted Shelly Palmer, professor at Syracuse University and CEO of The Palmer Group.

Agent management solutions on the market include Google Vertex AI Agent Builder, Amazon Bedrock Agents, Microsoft 365 Copilot, Decagon AI, and Sierra AI, serving various purposes from orchestrating systems to multi-agent automation. 

Also: The 5 myths of the agentic coding apocalypse

These platforms are essential to the future of agentic automation. The key to success is to "treat agents as infrastructure rather than features," said Diptamay Sanyal, principal engineer at CrowdStrike. 

Agents aren't one-off builds. "The problem is you end up with dozens of agents with no shared context model, no consistent governance, and no reusable patterns," Sanyal said. "A proper management platform gives you composable primitives, multi-tenant isolation, model routing across LLM providers, and observability into what agents are actually doing."

Fighting sprawl 

With agents multiplying by the millions, handling everything from sales to software development, the big hurdle is that they all want access to the same data.

"This creates an AI governance challenge," said Manu Narayan, CIO at GitLab. "If you don't build your AI stack intentionally, you could end up with dozens of vendors, and all of their agents, holding the keys to the kingdom."  

Also: How to build better AI agents for your business - without creating trust issues

This situation leads to agent sprawl, "a fragmented ecosystem of loosely managed agents with inconsistent behavior, duplicated functionality, and unclear ownership," said Yash Vijay Patil, software engineer with Texas A&M University. "Without strong governance, this sprawl can lead to operational inefficiencies and increased risk exposure."  

Many vendors and internal teams are building agent solutions for specific use cases, but often they lack shared identity models, lifecycle policies, or risk frameworks, said Monika Malik, a lead data and AI engineer at AT&T. "That approach creates duplication, inconsistent behavior, hidden costs, and security exposure. The problem will not be too few agents, but too many unmanaged ones."

Then there is the complexity of agent networks exacerbated by the popularity of consumer options like OpenClaw, said Brian Jackson, principal research director at Info-Tech Research Group. "It's safe to assume some employees will try to automate their work tasks with those. This leads to a problem in tracking all the agents you have deployed in the enterprise environment. While different management platforms claim they can discover the agents deployed in your system, the truth is that they are limited by the identity management layer."  

Agent management platforms offer benefits such as observability, so you know which agents you're using and what they're doing, Jackson said. 

Also: Give your 'human-level agents' a proper head start with these 3 best practices

In addition, these platforms enable governance by "using a central policy to set guardrails for what agents can and can't do and keep them aligned with enterprise goals." Ultimately, these systems enable value realization, as they "monitor performance over time and ensure agent costs and outputs fall within expectations, and add value to work," he added.  

The role of such management platforms is to "provide a control layer for how organizations deploy, monitor, secure and enhance their agents over time," said AT&T's Malik. "The major advantage of these platforms is not just orchestration, but operational discipline: visibility into what agents are doing; where they are pulling data from; how they are making decisions; when human oversight is required."

Understanding market trends

However, the competition between vendors to own the agentic management space is fierce, Jackson observed. 

"It will be a strategic position where enterprises are building their workflows and crafting deeper ties into an ecosystem," he said. 

Also: 5 security tactics your business can't get wrong in the age of AI - and why they're critical

Consequently, many agent implementations will be tied to familiar systems of record within varying lines of business, Jackson continued. "You end up with a situation where marketing is managing agents out of what used to be the CRM platform, while IT is managing agents from an asset management and observability platform."

"Consolidation through agent management platforms helps," he said. "They establish the context, permission models, security controls, and data boundaries that simplify agent orchestration at scale. Combining this type of platform with a hub-and-spoke model can help you become more intentional across your AI stack without slowing adoption speed."

Implementing the tech

Another challenge with agent management platforms is "they are harder to change than most cloud choices because they shape workflows, integrations, permissions, and operating models," said Malik. 

That situation is why adopting agents needs to be an enterprise decision. All stakeholder departments -- from engineering to security to legal to data governance to business owners -- need to be involved in decisions about the agent management platform. "The primary obstacle is averting fragmented adoption. Organizations should view agent platforms as long-term operating infrastructure, not just another purchase of an AI tool," said Malik.

Agent platform decisions are difficult to reverse because they are deeply embedded in workflows, data pipelines, and business logic, said Patil. "Evaluate platforms based on interoperability, extensibility, vendor lock-in risks, and support for open standards. Crucially, decisions should not be left solely to engineering -- cross-functional stakeholders, including security, data, and business leaders, must be involved."  

Also: Why enterprise AI agents could become the ultimate insider threat

In addition, professionals should remember that it's already difficult to get "data and workflows out of legacy software platforms," said Jackson. "Adding an AI layer on top of that means that the integration goes even deeper into the platform. Trying to migrate an agent management system will be like trying to perform a brain transplant."  

Businesses should, therefore, prioritize flexibility when moving to an agent management platform. "Evaluating where you are comfortable placing bets on platforms, versus trying to set up on a self-hosted platform," said Jackson. 

"Given the unpredictability of consumption costs for agentic workloads, it may be wise to architect a system that leverages internal infrastructure and avoids tying business processes to metered charges or consumption-based pricing."

Professionals should also treat the development and implementation of an agent management platform "like a database selection, not a SaaS tool evaluation," said Sanyal. "Involve platform engineering, security, and legal from day one. Not after the pilot succeeds. Plus, the decision shouldn't sit with a single line-of-business owner. It needs platform engineering, security, and whoever owns your identity and access model in the same room."