Every tech conference I’ve attended in the past year has featured breathless presentations about AI agents that autonomously handle complex workflows, make decisions, and collaborate with each other like a digital workforce.

Then I talk to the engineering teams actually building these systems, and the picture looks different.

There’s a gap between the AI agent narrative and AI agent reality. Understanding that gap matters for anyone planning technology investments.

The Conference Version

The story goes like this: AI agents are autonomous systems that perceive their environment, make decisions, and take actions to achieve goals. They can break down complex tasks, use tools, collaborate with other agents, and handle unexpected situations gracefully.

In the demo, an agent receives a request like “analyze our competitor landscape and recommend market positioning,” then proceeds to search the web, synthesize information, compare against internal data, and produce a strategic document. All without human intervention.

It looks remarkable.

The Production Reality

Most AI agents running in production today are considerably more constrained. They handle well-defined tasks with clear boundaries. They require substantial guardrails. They fail in predictable ways that humans need to catch.

A financial services firm I spoke with has an “agent” that processes incoming documents and routes them to appropriate departments. It works well—better than their previous rules-based system. But it’s not making autonomous decisions about financial strategy. It’s classifying documents.

A retail company runs an “agent” that responds to customer inquiries, escalating to humans when confidence drops below threshold. Useful automation, but far from the autonomous decision-maker of conference presentations.

These are valuable applications. They’re just not the science fiction version.

Why The Gap Exists

Several factors explain the distance between demos and deployments:

Reliability requirements. Enterprise systems need to work consistently. An agent that succeeds 90% of the time might be impressive as research, but a 10% failure rate is unacceptable for many production applications. Achieving 99%+ reliability constrains what agents can do.

Accountability challenges. When an agent makes a consequential decision, who’s responsible if it’s wrong? This question doesn’t have clear answers, so organisations limit agent autonomy to areas where mistakes are recoverable.

Integration complexity. Agents need to connect with existing systems. Most enterprise IT environments aren’t designed for AI agents to access and modify data freely. The plumbing work to enable agent actions often exceeds the agent development itself.

Cost considerations. Running sophisticated AI agents at scale isn’t cheap. The compute costs for an agent that continuously reasons about complex problems can exceed the labour costs it theoretically replaces.

What’s Actually Working

The successful agent deployments I’ve observed share common characteristics:

Narrow scope. They do one thing or a small set of related things. Not general-purpose autonomy, but specific task automation.

Human oversight. Rather than fully autonomous operation, they suggest actions for human approval or handle routine cases while escalating exceptions.

Graceful degradation. When they encounter situations they can’t handle, they fail safely rather than proceeding with low confidence.

Clear ROI. They’re deployed where the business case is obvious—high-volume, repetitive tasks with predictable patterns.

The Honest Outlook

AI agents will improve. Reasoning capabilities are advancing. Cost efficiency is increasing. Integration frameworks are maturing.

But the gap between what’s promised and what’s deployed suggests companies should be skeptical of vendor claims about agent capability. Ask for production references. Understand failure modes. Start with constrained applications and expand as you learn.

The technology is genuinely useful today. It’s just not magic. The organisations getting value from AI agents are the ones treating them as sophisticated tools requiring human partnership, not autonomous workers requiring only an initial prompt.

That’s less exciting than the conference talk. It’s also more realistic.