AI agents have had relatively easy jobs so far. They’ve been summarizing content, drafting basic emails and reports, providing web-based insights, and taking lightweight actions—all by themselves.
But in 2026, many industry observers believe that as these lone, autonomous agents spread across the enterprise, they’ll begin collaborating and acting on larger projects, from orchestrating targeted, cross-channel marketing campaigns to identifying high-intent prospects, managing early outreach, and handling customer issues start to finish.
Such multi-agent scenarios are where things get tricky. As digital workers trade data in real time, networks could clog, memory and storage could strain, and information could fragment. In other words, IT systems built before the rise of generative AI could bog down or fail, undermining agentic projects and leading executives to question return on investment (ROI). In fact, Gartner predicts 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls.
Because vendors themselves won’t have readymade solutions for orchestrating multiple agents, Forrester predicts most enterprises will turn to something entirely new: agentlakes. The analyst firm defines agentlakes, a term it coined, as composable architectures for managing and orchestrating “fractured AI agent deployments and enabling complex multi-agent use cases.”
The firm said rapid advances in agentic coding tools are proliferating, and the data supporting context engineering for agents will drive the need to provide real-time support for multimodal, multisource data in ways that existing platforms do not.
Tested Idea, New Approach
At first glance, it sounds a lot like data lakes, which centralize massive, diverse, and unstructured data. The difference is that agentlakes would be designed to help agents discover, access, govern, and act on that information, rather than merely storing it.
The concept has been around a while. And while not everyone loves the new name because of its data center or “data swamp” connotations, most agree it generally makes sense.
“We absolutely do need something that will integrate and help agents interoperate and tame their sprawl, though I’m not convinced ‘agentlake’ is the perfect metaphor,” said Andrew Brust, founder and CEO of another analyst firm, Blue Badge Insights.
“The definition of agents is going to vary from vendor to vendor, and all the tool chains and technologies behind them will be pretty fragmented.”
Those cracks become apparent when agents try to traverse existing security and management controls to access the data they need to function. Agentlakes help abstract complexity, giving agents governed, reliable access without forcing them through layers of manual gatekeeping.
“Agents only work if you’ve got the data to train them and operate,” said Nick Heudecker, a former Gartner data and analytics analyst who now leads strategic marketing at Cribl, a telemetry data management platform.
In security operations, he said, third-party agents often rely on continuous queries to platforms like CrowdStrike to detect errors and anomalies. Over time, that kind of access becomes harder to sustain as vendors tighten controls.
“Eventually, apps with the data, like Datadog and CrowdStrike, are going to choke off access,” Heudecker said. “So, what do you do? Your only real option is to build an agentlake.”
Without that shared foundation, he said, organizations end up fighting over data ownership instead of improving automation.
“It’s not about the agents. It’s never going to be,” Heudecker said. “It’s always going to be about the data and who owns it.”
In theory, agentlakes promise to solve several problems at once.
They offer a shared access layer that reduces redundant pipelines, mirroring how vendors such as Nutanix have built hybrid data and management platforms around centralized governance. They provide a central environment for coordinating workflows and sequencing tasks. They also create a clearer inventory of which agents exist, what they do, and how they interact.
For Carl Olofson, a former IDC analyst and IT consultant at DBMSGuru, organizational clarity is essential.
“The idea of registering agents and creating a taxonomy of what they are and what they do has been something we’ve been kicking around for a while,” Olofson said. “Teams may also find themselves creating duplicate or conflicting agents.”
Without it, teams spend much of their time simply figuring out where logic lives and how systems connect, he added.
A Bumpy Road to Implementation
Still, the path to successful agentlake deployments won’t be smooth.
For one thing, they will require a level of data discipline that many organizations have never had to maintain, said Heudecker. Incoming information must be accurate, consistently formatted, and automation-ready. Unlike traditional data lakes, which absorb messy inputs and sort them out later, agentlakes need high-quality data and clearly defined access rules from the start.
Performance is another pressure point, Heudecker said. Most data platforms already run close to capacity, supporting batch jobs and a small number of human users. Agentlakes would have to handle hundreds, or even thousands, of AI agents operating simultaneously, placing unprecedented strain on storage and networks. Without high-throughput infrastructure, systems quickly hit hard physical limits, Heudecker said.
Then there is the problem of freshness.
For years, many organizations have treated static dashboards and aging datasets as acceptable sources of truth, Heudecker said. Agent-driven systems can’t afford that luxury. The need for continuously updated data will force companies to rethink how it’s refreshed, synchronized, and governed across digital environments.
Identity and access management (IAM) could be another looming challenge.
Wendy Nather, senior research initiatives director at 1Password, noted many organizations already struggle to manage network access for human employees. Adding large numbers of autonomous AI agents, even within an agentlake, risks turning credential management into a serious bottleneck.
“If somebody sets up an agent and it has to go through security to get credentials, that forces consolidation,” she said. Without centralized controls, agent sprawl becomes difficult to contain.
Pacing Deployments
For now, most experts argue that restraint matters more than ambition.
Instead of rushing to centralize data in new platforms, they recommend starting with virtualization and federation layers, exposing existing systems to agents through governed interfaces rather than large-scale migrations. Early projects, they said, should focus on narrow use cases and controlled pilots, not enterprise-wide reinventions.
Governance should come first. Before scaling agent deployments, organizations must understand who and what has access to critical systems, how permissions are granted, and how activity is monitored. Without that foundation, automation amplifies risk.
Brust said enterprises should also resist the urge to wait for a turnkey solution.
“There isn’t really anything to buy quite yet,” he said. “Customers are going to have to articulate what they need.”
In practice, that means treating early agent systems as supervised tools rather than autonomous workers. Human review, approval, and oversight remain essential.
Hybrid infrastructure adds another layer of complexity. Most enterprises operate across on-premises systems and multiple clouds, and agent platforms must function consistently in all of them. Fragmented governance in those environments only increases operational friction. For those reasons, some infrastructure providers, including Nutanix, are positioning their hybrid platforms as foundations for running agent-driven workloads consistently across environments.
Beyond the Metaphor
Whether “agentlake” becomes a lasting term is uncertain. The problem it describes is not.
As AI systems become more autonomous, enterprises will be forced to rethink how autonomous agents are discovered, orchestrated, and secured. Vendors, in turn, will need to offer agentlakes—or something like them—to efficiently address that, experts said.
“The idea really has a lot of merit,” said Heudecker. “Competitive pressures alone are going to push companies in this direction.”
For many organizations, the real risk is not failing to adopt agentlakes. It is deploying fleets of AI agents faster than they can understand, govern, or control them.
David Rand is a business and technology reporter whose work has appeared in major publications around the world. He specializes in spotting and digging into what’s coming next–and helping executives in organizations of all sizes know what to do about it.
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