The pitch is everywhere now. AI agents that book your travel, manage your calendar, handle your email, coordinate your projects. Autonomous systems that take a goal and figure out how to accomplish it. Tell the agent what you want. It handles the rest.

The demos are impressive. I’ve watched agents navigate complex multi-step workflows, make decisions, recover from errors. The technology clearly works.

And yet.

Talk to anyone actually deploying these systems in production and you hear a different story. Impressive in controlled conditions. Brittle in the real world. Requires more human supervision than the marketing suggested. Works great until it doesn’t, and when it doesn’t, it fails in ways that are hard to predict and harder to fix.

There’s a gap here. A big one. And understanding why it exists is more useful than pretending it doesn’t.

The Autonomous Vehicle Problem⌗

Here’s an analogy that keeps coming up in conversations about agents: self-driving cars.

Early autonomous vehicles could maintain lane position. They could follow a preset route under ideal conditions. The demos looked like the future. But the gap between “works on a closed course” and “works in downtown Boston during a snowstorm” turned out to be enormous.

AI agents are in roughly the same place. They can execute predefined sequences reliably. They can handle tasks with clear boundaries and predictable inputs. The moment you introduce ambiguity, edge cases, or systems that don’t behave exactly as expected, things get interesting.

This isn’t a criticism. It’s a description of where the technology actually is versus where the keynotes imply it is.

Why Agents Break⌗

Most failures I’ve seen come down to one of three problems.

The specification problem. The agent needs to know what success looks like. Not vaguely - precisely. “Handle my email” isn’t a specification. It’s a wish. Which emails matter? What counts as “handled”? When should it escalate versus decide? Most people can’t answer these questions clearly for themselves, let alone for an autonomous system.

This is the requirements gathering problem I’ve written about before, just at a higher level. If you can’t explain what you want to a competent human, you can’t explain it to an agent either. The agent just makes the failure mode faster.

The connection problem. Agents need to interact with real systems - calendars, databases, APIs, file systems. Each connection is a potential failure point. Authentication expires. APIs change. Rate limits hit. Data formats drift.

The industry is working on this. Anthropic’s Model Context Protocol is becoming a standard for how agents connect to tools. But standardization is slow, and most enterprise systems weren’t designed with autonomous agents in mind. The integration work is brutal.

The judgment problem. Some decisions require context that’s hard to articulate and harder to encode. When should the agent push back versus comply? When is a shortcut acceptable versus dangerous? When does a small anomaly indicate a big problem?

Humans develop this judgment through experience. We pattern-match against thousands of situations we’ve encountered. Agents don’t have that depth. They have training data and inference. Sometimes that’s enough. Often it isn’t.

The Framing That Actually Helps⌗

Here’s what I’ve noticed separating successful agent deployments from expensive disappointments: the framing.

Companies treating agents as autonomous replacements are struggling. Companies treating agents as “trainees who need supervision” are getting value.

This sounds like a downgrade. It isn’t.

A trainee who can reliably handle 40% of the routine work - the mechanical, predictable, specification-friendly parts - while escalating anything ambiguous to a human is genuinely useful. That’s not a failed autonomous agent. That’s a successful augmentation tool.

The mistake is expecting full autonomy and being disappointed by augmentation. If you expected augmentation from the start, you’d be thrilled.

What This Means Practically⌗

If you’re evaluating AI agents - for your work, your team, your company - here’s the calibration that seems to match reality:

Narrow, well-defined tasks: Agents work well here. Data transformation, routine communications with clear templates, monitoring with explicit thresholds. Tasks where “what good looks like” can be specified precisely.

Broad, judgment-heavy tasks: Still needs humans. Strategy, complex negotiations, anything where the right answer depends on context that’s hard to articulate. Agents can assist - gathering information, drafting options, handling logistics - but the core decisions stay with people.

The middle ground: This is where the interesting work is happening. Tasks that are mostly routine but occasionally require judgment. The agent handles the routine parts and flags the exceptions. Humans review the flags and handle edge cases. Neither could do the job alone. Together they’re more capable than either.

This is the same pattern that makes “vibe coding” work. The AI handles execution. You provide judgment about what’s worth executing. The tool is powerful precisely because it doesn’t try to replace the parts that humans are still better at.

The Expectation Reset⌗

2025 was the year everyone got excited about agents. 2026 is shaping up to be the year expectations calibrate to reality.

That sounds pessimistic. It’s actually good.

Unrealistic expectations lead to expensive disappointments and abandoned projects. Calibrated expectations lead to deployed systems that actually help. The technology doesn’t change. The framing does. And the framing determines whether you get value from it.

An AI agent that reliably handles 30% of your routine work isn’t a failed autonomous system. It’s a successful tool. A good one, actually. The kind that compounds - 30% of routine work, every day, freed up for things that require actual thought.

The keynotes will keep promising full autonomy. The reality will keep delivering sophisticated augmentation. The people who thrive will be the ones who wanted augmentation all along - or at least learned to want it once they understood what was actually on offer.

The gap between keynotes and reality isn’t a scandal. It’s just the normal distance between marketing and engineering. The agents aren’t failing. They’re working exactly as well as they currently can.

The only question is whether your expectations match that reality or fight against it.