The Lonely Agent Problem: Why Billions in Enterprise AI Agents Will Sit Idle (and 5 Things to Build Instead)

Ryan Gavin, CMO of Slack at Salesforce, coined the term that captures 2026's coming hangover: "the year of the lonely agent." Companies will spin out "hundreds of agents per employee," he predicts, but most will sit idle—impressive but invisible.

It's a pattern enterprise software has seen before. In the 2010s, companies hoarded SaaS licenses that nobody used—so-called "shelfware." The waste was staggering: zombie licenses, auto-renewals, overlapping tools doing the same thing. Now replace "SaaS licenses" with "AI agents" and the problem is even worse. Where IT sprawl meant paying for unused licenses, agent sprawl burns GPU cycles, engineering hours, and API credits on redundant or idle agents.

The data backs this up. More than 4 in 5 IT leaders believe the proliferation of AI agents will yield more complexity than value, according to Salesforce's 2026 Connectivity Benchmark Report. And Gartner predicts over 40% of agentic AI projects will be canceled by end of 2027 due to escalating costs, unclear business value, or inadequate risk controls.

The gap between an AI agent demo and a production deployment is enormous. Salesforce demos sell the idea of building an agent "in under 30 minutes," but getting that agent production-ready takes weeks if not months. Most enterprises build pilots that work in isolation, then realize they can't scale without rebuilding everything.

AT&T's chief data officer Andy Markus explains the core challenge: "In an agentic solution, you're breaking down the problem into many, many steps. And the overall solution is only accurate if you're accurate each step of the way." One weak link in the chain and the entire agent becomes unreliable.

Here's the counterintuitive insight most people are missing. The lonely agent problem isn't a reason to avoid AI agents—it's the biggest opportunity for indie hackers in 2026.

Think about it historically. Stripe didn't build the internet—it built payments for the internet. Datadog didn't build the cloud—it built monitoring for the cloud. The pattern repeats every technology cycle: the real money is in the picks and shovels, not the gold mine itself.

The AI agent market is exploding to $47 billion. Enterprise spending on AI infrastructure will exceed $470 billion in 2026. And every single one of those deployments will face the lonely agent problem—agents deployed without proper monitoring, management, or optimization. That's an enormous gap waiting to be filled by builders who move fast.

Before you dismiss AI agents entirely, the numbers tell a more nuanced story. Despite slow adoption, Salesforce Agentforce now serves 18,500 enterprise customers—up from 12,500 the prior quarter. Customers run over three billion automated workflows monthly. Agentic product revenue hit $540 million in ARR.

Gartner itself predicts at least 15% of day-to-day work decisions will be made autonomously by AI agents by 2028, up from 0% in 2024. And 33% of enterprise software will include agentic AI by 2028. The agents are coming—the question is whether enterprises can manage them effectively.

AI agents are at the "peak of inflated expectations" on Gartner's Hype Cycle and heading into the "trough of disillusionment" throughout 2026. That's exactly when the real building begins.

The pattern is clear: hyperscalers spend $470B+ on AI infrastructure, enterprises race to deploy agents, 40% of those deployments fail, and the companies that solve the failure modes capture the next wave of value. That's the playbook for indie hackers right now.