I’ve spent the last three months talking to innovation managers at 40 different companies about their AI agent pilots. The results weren’t what I expected.

The hype cycle around autonomous AI agents hit fever pitch in late 2024. Every vendor promised software that could think, plan, and execute complex tasks without human intervention. VCs poured billions into the space. But here’s the thing - most of what’s shipping doesn’t look anything like the demos.

What’s Actually Working

Let me be specific about where I’m seeing real traction.

Customer support triage has become the quiet success story. Not fully autonomous agents handling entire conversations, but smart routing systems that can understand customer intent, pull relevant context from multiple systems, and either resolve simple queries or prepare comprehensive handoffs for human agents. Klarna’s reported a 25% reduction in average handling time using this approach. That’s not revolutionary, but it’s real money.

Code generation assistants are the other obvious winner. GitHub Copilot’s enterprise numbers suggest about 40% of code at participating companies now gets AI assistance. But notice the framing - assistance, not replacement. The agents that work are the ones augmenting human decision-making, not trying to replace it entirely.

Document processing pipelines round out the top three. Insurance claims, legal contract review, regulatory filings - anything involving structured extraction from unstructured text. These aren’t glamorous applications, but they’re generating measurable ROI.

Where Things Get Murky

The autonomous agent dream - software that can independently plan and execute multi-step workflows - remains largely theoretical in production environments.

I talked to the head of AI at a major Australian bank (they asked not to be named). They’d invested eighteen months building an autonomous agent for loan processing. The system could handle about 30% of applications end-to-end. The problem? The remaining 70% required human review anyway, and the edge cases the agent got wrong created more work than they saved.

“We’re essentially back to using it for triage and data extraction,” she told me. “The autonomous decision-making piece just isn’t reliable enough for regulated environments.”

This pattern repeated across my conversations. Companies start with ambitious autonomous agent projects, then quietly descope to more targeted automation use cases.

The Integration Tax

Here’s something that doesn’t get enough attention: most enterprise agent deployments fail not because the AI isn’t capable, but because the integration work is brutal.

An AI agent that needs to interact with your CRM, ERP, ticketing system, and document management platform requires connectors to all of them. Those connectors need to handle authentication, rate limiting, error recovery, and data format differences. You’re not just deploying an agent - you’re building a small integration platform.

I’ve seen companies spend more engineering hours on the plumbing than on the AI itself. That’s not necessarily wrong, but it’s a reality check for anyone expecting quick wins.

The Reliability Question

Autonomous agents make mistakes. That’s not controversial. The question is how you handle those mistakes in production.

Traditional software fails in predictable ways. An API returns an error code, you catch it, you have a fallback. AI agents fail in unpredictable ways. They misunderstand context, hallucinate facts, or take logical-seeming actions that turn out to be wrong.

The companies seeing success have invested heavily in guardrails - human-in-the-loop checkpoints, confidence thresholds that trigger escalation, comprehensive logging for post-hoc analysis. This infrastructure isn’t optional if you’re putting agents anywhere near customer-facing processes.

What I’d Tell Innovation Managers

If you’re evaluating AI agents for your organization, here’s my honest take:

Start with augmentation, not autonomy. The technology works when it’s helping humans work faster. It struggles when you try to remove humans from the loop entirely.

Budget for integration. Whatever you think you’ll spend on connecting systems, double it. Then add contingency.

Pick boring use cases first. Document processing, data extraction, triage and routing. These aren’t exciting, but they’re where the technology actually delivers.

Build monitoring from day one. You need to know when your agent is struggling before your customers do.

Be skeptical of vendor demos. That slick autonomous workflow they showed you probably doesn’t account for your legacy systems, your edge cases, or your compliance requirements.

Looking Forward

I’m not bearish on AI agents - quite the opposite. The underlying models keep improving. The tooling is maturing. We’re seeing the emergence of actual standards for agent-to-agent communication.

But the path to autonomous enterprise AI runs through a lot of boring infrastructure work and incremental deployment. The companies that understand this are the ones that’ll actually capture value. The ones chasing fully autonomous agents before the foundations are in place will burn budget and credibility.

The technology is real. The timelines in those vendor pitches probably aren’t.

If you’re running agent pilots and want to compare notes, reach out. I’m always interested in hearing what’s working - and what isn’t - in the real world.