Vercel deleted 80% of its agent's tools and the agent got better + what to delete from yours (guide inside!)

I learned maintenance first from boats.

Not as a theory. As a job. I maintained boats in Indonesia, where saltwater finds every shortcut and the difference between “probably fine” and fine gets very real once you are away from shore. You look at lines, fittings, pumps, batteries, corrosion, and weather differently when the thing you are maintaining is also the thing that has to bring you back.

I also watched planes get maintained there, then climbed into them hoping the work had been done well. Mostly it had. I mean that literally: one engine memorably failed over the jungle once, and it was, in the end, fine. Not because failure is harmless. Fine because the plane stayed a plane, the people knew what to do, and there was enough care and margin in the system that the failure stayed local.

That is what “mostly” means in maintenance. Things still break. The point is that the thing has been cared for well enough that when something breaks, the failure stays small.

The agents you have already built will keep producing work long after they stop being right. Keeping them honest is about to be one of the most valuable AI skills there is.

Maintenance is one of those words that sounds dull until you depend on it. Then it becomes intimate. You notice the sound that was not there yesterday, the frayed edge, the mechanic’s face. You learn that care is not a feeling. It is inspection, memory, habit, replacement, skepticism, and respect for the ways things fail.

There is a Barry Lopez line I have carried around for years, from the end of “The Orrery”:

If one is patient, if you are careful, I think there is probably nothing that cannot be retrieved.

The word I keep is retrieved — not fixed, not replaced — but stayed-with long enough to bring back.

That is the spirit this AI conversation is missing.

We talk about agents as if the hard part is getting them to exist. Build the agent. Launch the agent. Connect the tools. Give it memory. Let it work.

But anything useful enough to depend on becomes something you have to maintain. That is true of boats. It is true of planes. It is true of buildings, institutions, data pipelines, customer-support systems, editorial standards, and software. It will be true of AI agents too.

Vercel’s sales agent story is easy to read the wrong way.

The obvious version is the labor story. Business Insider reported that Vercel trained an AI agent on one of its best sales development reps, used it to handle much of the inbound sales workflow, and moved from a ten-person inbound team to one person overseeing the agent while the rest shifted into more complex outbound work.

But often the biggest story isn’t the most useful one.

The more useful story is what had to be true around the agent for the work to become trustworthy. Vercel did not just tell a model to “do sales.” Engineers watched a strong rep. They documented the workflow. The agent filtered inbound messages, qualified leads, researched companies, drafted responses, routed support questions away from sales, and had a human reviewing its work in Slack.

In other words, the agent had a workbench. It had sources. It had tools. It had a defined job. It had handoffs. It had a review path. It had feedback. It had a human who could see what was happening. The agent was not a free-floating brain. It was a system around delegated work.

That is the part most people still miss.

The obvious question is, “Can I build an agent?”

The better question is, “What workbench does this agent need?”

The mature question is, “How do I keep that workbench healthy after the agent starts working?”

That third question is agent maintenance. And it is about to matter more than the building, because delegated intelligence creates a maintenance surface. Once a system reads context, calls tools, remembers preferences, drafts work, or touches a workflow other people depend on, someone has to keep the setup around it fit for the job.

Here’s what’s inside:

• The two ways agents break. One when the world around them drifts, and one stranger failure: the model underneath them gets better, and the harness built for its old weaknesses turns into dead weight.

• Why “more” is the wrong instinct. More context, more tools, more memory feels like care. Usually it is the thing rotting your agent from the inside.

• The seven parts that go stale. Job, diet, memory, tools, reach, proof, and value — the harness around the model, and the specific way each one fails before you notice.

• Five agents, maintained in the open. A writing agent, a product-backlog agent, a Codex workflow, a support and revenue-risk agent, and a content pipeline, each shown drifting and each pulled back.

• The loop I run before I trust one again. The short, plain maintenance pass I walk before letting any agent stay close to real work.

• The audit, ready to run. The loop turned into a guide you can point at a live agent today: the last ten runs, the seven surfaces, and a keep, change, pause, or retire call before you trust it again.

Below, the seven parts of the harness, what breaks where, and the loop I would run before trusting any agent that is part of the work.