# Once agents do the middle of the work, your people should own both ends. · VANDAM

> Agents do the middle of a task; people own the start and the end. How to redesign a role around intent, review, exceptions and accountability.

URL: https://vandam.ai/notes/ai-does-the-middle  
Contact: hendrik@vibelab.be

# Once agents do the middle of the work, your people should own both ends.

Balaji Srinivasan’s line that AI works middle-to-middle is the most useful frame I know for redesigning a role. This note covers what sits at the start and the end of a task, what 1,500 workers told Stanford they want, and how I would rewrite one production manager’s morning.

Hendrik De Winne 25 October 2026 7 min read

In January [Alex B](https://x.com/bprintco/status/2012595972090605685), owner of Brushworks Services Co., tried to explain Claude Cowork “for the guy who owns a landscaping company with 20 employees.” His example is the production manager. Every morning that man “logs into Housecall Pro or Jobber,” “reviews the schedule,” “assigns crews,” “decides what equipment goes where,” “determines arrival times,” “messages foremen” and “confirms labor and inventory.”

Then Alex trains Claude on “your SOPs” and “how you make those decisions,” and Claude opens the CRM, assigns the crews, schedules the equipment and notifies everyone. “Automatically. Every morning.” His conclusion is one sentence: “Your production manager is now just an exception handler.”

The post is a thought experiment. It starts with “Let’s say,” and nobody measured anything. I still find it the most useful picture of agent work I have seen this year, because it is small enough to hold in your head. The only word I would change is “just.” Handling the exceptions was always the hardest part of that job, and once the agent takes the routine, it becomes most of the job.

## Agents take the middle of a task, people keep the ends

The cleanest description of this shape came from Balaji Srinivasan, author of The Network State, and was spread by [David Sacks](https://x.com/DavidSacks/status/1952026572661092829), founder and investor at Craft Ventures, in August 2025.

> AI models still need to be prompted and verified, often iteratively, to drive business value. As @balajis says, AI is middle-to-middle, not end-to-end. Humans do the stuff at the ends (supervision); AI does the stuff in the middle.
>
> David Sacks, Founder and investor, Craft Ventures, August 2025, [Source](https://x.com/DavidSacks/status/1952026572661092829)

Sacks used the line to argue that fears of AI job loss are overhyped. I care less about that debate than about the design brief hidden in it. If agents do the middle, a company has to decide on purpose what sits at each end, who owns it and how much time it gets. Most companies skip that decision. The agent goes live, the old role stays on paper, and the person in it works out alone what is left.

[Annelies Gamble](https://x.com/AnneliesGamble/status/2069814714214023186), a partner at [Zetta Venture Partners](https://x.com/AnneliesGamble/status/2069814714214023186), gave the ends their contents in a split I already used in [an earlier note](https://vandam.ai/notes/ai-needs-hr-and-finance): rules go to deterministic automation, judgment under context goes to agents, and “Humans are for accountability, ambiguity, and trust.” Read next to Balaji’s line, that tells you what each end is made of.

## The start is intent and context

At the front of every task someone decides what the work is for and what good looks like. In his list of [30 features of an AI-native company](https://x.com/businessbarista/status/2094213970215231831), Alex Lieberman, co-founder of Morning Brew and of the AI firm Tenex, describes coding agents that handle “planning, writing, testing, reviewing, and shipping code while humans define the intent and acceptance criteria.” A few items later he generalizes it: “Human touch and judgement gets reserved for the first and final mile of most processes.”

His notes from a call with the person who leads Labs at LangChain make the start concrete. The example agent logs a customer meeting, “writes follow-up draft and stops before sending.” The eval checks “if the agent paused before sending.” And the line I would pin above every AI project: “Highest leverage organizational act: humans writing down what good looks like.”

For the production manager, the start is everything Alex B waves through with “your SOPs.” Which client gets a crew first after two days of rain. Which foreman never gets the old mower. How much slack goes into a Friday. He carries those rules in his head today, and the agent only gets them if he writes them down.

## The end is review, exceptions and a name

At the back of every task someone checks the work, catches what broke and answers for the result. [Greg Isenberg](https://x.com/gregisenberg/status/2083175325098266931), CEO of the studio [Late Checkout](https://latecheckout.studio/), put it in a list of opportunities this summer: “everyone generates infinite drafts now, the bottleneck moved to reviewing and choosing.” After two days with founders in San Francisco in October, [he described](https://x.com/gregisenberg/status/2110414696037716372) a vertical agent harness as four things, the last of which is “a clean screen for a human to review. The model is maybe 20% of it.”

[Anthropic](https://www.anthropic.com/research/employment-at-anthropic) found the same thing inside its own walls: “This acceleration makes human attention the bottleneck for agent output.” I read that report closely in [another note](https://vandam.ai/notes/inside-anthropic). Its lesson for a landscaping company is the same as for a lab. When the middle speeds up, the ends get the queue.

The same Isenberg post has the best description of an exception I have found, in his account of a robotics founder’s setup: “Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.” Each exception is a gap in the rules at the start. Handled well, it closes that gap, so fewer cases come back next month.

## Workers drew the line in roughly the same place

The best evidence on where people want to stay comes from a [Stanford team](https://arxiv.org/abs/2506.06576) that includes the economist Erik Brynjolfsson. They asked 1,500 workers in 104 occupations about 844 tasks, and rated each on a five-level Human Agency Scale. Level H3 means the “AI agent and human form equal partnership, outperforming either alone.”

- **46.1%**
  Of tasks where workers were positive about automation by an AI agent.
- **69.4%**
  Of pro-automation answers that gave “freeing up time for high-value work” as the reason.
- **45.2%**
  Of occupations where equal partnership was the level workers wanted most. Source: [Shao et al.](https://arxiv.org/abs/2506.06576), 2025, revised 2026.

So workers are happy to give away a large share of the middle, as long as they keep a real part in the outcome. The paper also finds that workers “generally prefer higher levels of human agency than what experts deem technologically necessary,” and its authors see key human skills shifting “from information processing to interpersonal competence.” Those are the skills at the ends: agreeing on what good means, telling a client the crew is late, deciding who gets the last truck.

## Six steps to rewrite the production manager’s job

This is how I would redesign the production manager’s role, and it applies to most roles where an agent takes over a daily routine.

1. **Write the start down before you build.** The manager drafts his decision rules and his test for a good schedule in plain language. These become the agent’s instructions and the checks that grade it. Plan for the first draft to be wrong.
2. **Hand over the middle in steps.** Lieberman’s ladder is “observe, suggest, act with approval, act alone.” In the first weeks the agent drafts tomorrow’s schedule and stops before any foreman gets a message. It moves up a step when the manager stops correcting it.
3. **Name the exceptions in advance.** Gamble calls for “exception taxonomies, escalation paths” and “approval thresholds.” Weather calls, a crew short, a client complaint, any change above a set cost: these always go to him, and the agent knows it.
4. **Put one name on the output.** Lieberman’s last feature is to “Trace every output to its prompt, model, data, and approver.” The schedule still goes out under the manager’s name, and he can see why the agent chose what it chose.
5. **Close the loop every week.** Each exception from the past week either becomes a new rule at the start or stays a judgment call he keeps. The list of exceptions should get shorter.
6. **Change the job on paper.** New description, new targets, and pay that reflects the judgment he now carries. This step needs HR and finance in the room, and it is the one companies skip.

## The second sale goes to the person in the role

In Gamble’s essay, Troy Shen, co-founder of Cervo, which builds AI for customs brokers, describes the problem that sinks step six: “Successful AI adoption takes two sales, not one. You’re selling an outcome to the executive and a dramatically better workday to the operator who’ll actually use the agents.” Without operator buy-in, Gamble adds, the deployment will fail.

“Exception handler” is a terrible pitch to the production manager. “You set the rules the crews run on, you decide every hard case, and you stop typing the schedule into Jobber at six in the morning” describes the same job, and he might actually want it. The Stanford numbers suggest most people would.

The strongest objection is that the ends shrink. Lieberman’s eval notes say it openly: “Human touch points shrink with every smarter model release,” and the goal is to “minimize human contact points on the loop.” Sacks’s line is fourteen months old, and Isenberg now writes that we are “maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions.” I take that seriously. A design that freezes today’s ends will age fast.

The answer I would give a client is to design the ends to move. As the agent climbs the ladder, the manager reviews less and sets more of the standard, which is where Lieberman’s ladder ends too, with “humans setting the standard instead of checking every answer.” Someone still has to decide what good means for this company, and someone still has to answer to the client.

Ultimately ensuring that today’s jobs are tomorrow’s tasks. (Aaron Levie, CEO of [Box](https://x.com/levie/status/2004654686629163154), December 2025)

Levie wrote that line after arguing that agents “require management, oversight, and substantial context to get the full gains.” In practice it means a job gets rebuilt around the tasks agents cannot finish alone. Before you buy an agent for any role, write one page for that role: what the person decides before the agent starts, what they check after it finishes, which cases always come back to them, and whose name goes on the result. If that page stays empty, the agent is not ready to go live.

## More notes

29 October 2026 6 min read

## Who owns what your AI learns.

Y Combinator now asks for startups that help companies own their intelligence. Before a mid-sized company owns a model, it can own the four things every model works with: context, evals, rules and connections.

26 October 2026 9 min read

## Delete the step first and give the agent whatever survives.

Musk’s five-step method puts automation last, and most AI programs start there because an agent is the one step a vendor can sell. I walk accounts payable through the steps in his order and show why an agent that clears exceptions fast hides the steps that should go.

## Your agents need a critic and so does the plan they carry out.

Models grade their own work too kindly, and the labs now separate the agent that makes from the agent that checks, the way finance separates maker and checker. I set out a four-layer review design with a minutes budget, and why the brief itself needs a critic with no stake in it.