The Gap Between Agent Hype and Reality
Over the past couple of months, it feels like every LinkedIn post is about agents. Agentic workflows, autonomous systems, AI that “does the work for you.” With projects like OpenClaw and others getting attention, it’s easy to think this is the next phase. Less prompting, more doing. But when you actually talk to teams trying to use this in practice, the reality looks very different.
Most “agents” today are still tightly scripted. A few steps, a few rules, an LLM in the middle. Useful in some cases, but far from autonomous. More importantly, they’re rarely tied to how real work actually gets done. The conversation is moving toward fully autonomous systems, but the actual implementation is still stuck at loosely connected tools and manual handoffs. That gap is where most of the friction lives.
We saw this firsthand recently while building an agent to review NDAs. Not a demo or a prototype, but something designed to handle a real workflow. An inbound NDA comes in, it’s compared against a defined rule set, redlines are applied, and a draft is returned. That’s it.
What made it work wasn’t autonomy or complexity. It was that the task was clearly defined, the inputs were known, and the output had a specific purpose. It fits into an existing workflow without requiring someone to rethink how they work or manage another system.
There’s another issue that isn’t getting talked about enough. For most non-technical users, agents feel overwhelming. It’s no longer just asking a question and getting an answer. Now it’s defining the task, configuring steps, selecting tools, monitoring outputs, and stepping in when something breaks. At some point, the tool that’s supposed to save time starts to feel like another system to manage. That’s not progress. It’s just shifting the work.
You’re already seeing this pattern play out with tools like Microsoft Copilot. Firms have access, but usage is inconsistent and rarely translates into real workflow change. The technology is there, but it’s not embedded in how work actually gets done.
The teams that will actually benefit from this shift aren’t the ones experimenting with the most advanced agents. They’re the ones quietly embedding simple, repeatable automation into real workflows. Not fully autonomous. Not overly configurable. Just clearly defined work getting done faster.
Agents will matter. But not in the way they’re being talked about right now. The real opportunity isn’t AI that does everything. It’s AI that fits cleanly into how teams already operate, without requiring them to become engineers.