News

The Rise of Autonomous Infrastructure

• By John K. Waters
• 04/04/2025

The post-LLM hype cycle is already collapsing under its own weight. Co-pilots are table stakes, and chatbots are yesterday’s toy. What’s next? AI agents, autonomous, persistent systems that don’t just answer questions but act. And according to a new report released by SnapLogic ("AI Agents: The Final Frontier of the Enterprise") enterprises are deploying them aggressively.

According to SnapLogic’s research, conducted in partnership with 3GEM and based on responses from 1,000 IT decision-makers in the U.S., U.K., Germany, and Australia, 50% of enterprises are already deploying AI agents, and another 32% plan to do so within the next 12 months. This isn’t a pilot wave—it’s a platform shift.

Unlike AI assistants that require constant human steering, AI agents operate with purpose: take a task, understand context, make decisions, and execute workflows—no hand-holding required. They’re multi-modal, stateful, and often API-native. They’re also reshaping how ops teams think about automation, especially across sprawling cloud infrastructure.

"Customer benefits elections is a lengthy and data-intensive process," said Aptia’s Mike Wertz, who rolled out SnapLogic’s AgentCreator to automate claims processing. "What once required hours of backend effort now happens in minutes."

Killing the Manual Slack Ticket, One Endpoint at a Time
Agentic AI is already saving serious time. Spirent Communications built a sales intelligence agent that cut manual prep hours by 25%. Independent Bank wired up real-time AI endpoints during an internal outage to auto-respond to users across voice and chat. Help desk calls dropped 20% in a single event.

No surprise then that IT was named the top beneficiary of agentic automation in the SnapLogic report—80% of respondents said their IT orgs will see the biggest ROI. And for good reason: IT teams today spend 16 hours per week managing and fixing AI tools. With agent-based automation, respondents expect to reclaim 19 hours weekly. That’s a full sprint per month, per engineer, clawed back from toil.

Other departments are next. Data, security, R&D, and even finance are increasingly reliant on manual, high-frequency ops. AI agents—especially when paired with infrastructure-as-code, APIs, and observability layers—are well-positioned to take the wheel.

Why LLMs Alone Won’t Cut It
The big unlock isn’t just generative text. It’s persistent logic with task execution built in.

Where LLMs excel at language and summarization, AI agents combine reasoning with memory, orchestration, and environment awareness. They don’t just say, "Here’s how you might reset your password." They actually reset it based on user policy, security posture, and real-time system state.

This makes them ideal for resolving the long tail of issues that used to clog IT queues, cost human hours, and never quite justified custom automation.

SnapLogic’s AgentCreator, for example, lets enterprises build and manage agents that plug directly into existing workflows. In Aptia’s case, this meant eliminating data entry overhead in insurance claims by mapping LLM-driven logic to backend systems via SnapLogic’s integration layer.

$$$, At Scale
According to the report, nterprise adoption isn’t speculative; it’s budgeted.

The average company surveyed plans to invest over $2.5 million in AI agents this year alone. A staggering 10% are spending more than $5 million. Australia is outpacing everyone, with companies there averaging over $3.15 million in planned spend.

And they’re not spending it all on proof-of-concepts, the researchers found. Enterprises today run an average of 32 AI agents in production. They plan to deploy 30 more over the next year.

That’s 60 agents per org—automating, orchestrating, optimizing. Think automated incident responders, FinOps cost monitors, self-healing infra agents, user access managers. Think low-latency, high-consequence task execution that scales beyond cron jobs and shell scripts.

What’s Blocking the Bots
Of course, real-world friction remains. Security and privacy are the #1 concern, cited by 60% of respondents. AI hallucinations still scare 14% of orgs. And legacy infrastructure is a recurring bottleneck, especially when agents need access to internal systems that weren’t built with API extensibility in mind.

Also: not everyone understands what an agent is. Nearly a third of respondents reported lack of internal knowledge as a deployment blocker.

Bridging this knowledge gap is where AI Centers of Excellence (CoEs) come in—small teams focused on governance, risk mitigation, and cross-team integration. It’s also why companies are starting with narrow, high-impact use cases (like IT workflows) before moving into riskier or more customer-facing domains.

Agentic OS: Coming Soon to an Org Near You
The key insight from SnapLogic’s report isn’t just that AI agents work. It’s that enterprise teams trust them. In fact, 84% said they’d trust an agent just as much, if not more, than a human to complete certain tasks.

That’s not to say humans are getting replaced. But the composition of teams is changing. Agents don’t need sleep, don’t forget context, and don’t skip documentation. They’re being plugged into pipelines, CLIs, CI/CD systems, and ticketing queues—wherever repeatable work lives.

We’re heading toward a future where AI agents are treated like any other service: deployed, versioned, monitored, and upgraded. AgentOps is the new DevOps. And the businesses that figure this out fastest won’t just be more efficient. They’ll be more adaptive, more resilient—and maybe even more human.