Human Agentcy

## Editorial

### Human Agentcy (Not a Typo)

Agents Are In Your Future

At first, and I mean tthree years ago, we asked models questions. Then, two years ago, we asked them to generate things: text, images, code, summaries, plans. Then, last year, we called them copilots, because they sat beside us while we worked.

Now the use case is changing once more. Humans can carry out thier daily tasks by deploying agents to do the work. The agents are not autonomous in the human sense. They do not decide what should matter. They do not originate purpose. But once directed, they can work alone. They can plan, call tools, inspect their own output, notice errors, retry, and sometimes repair the path.

This is Agent cy.

Agentcy is an extension of human agency, not a replacement for it. It is the extension of human intention through agents: software systems that can act, inspect, and recover once directed. A chatbot responds. A copilot assists. An agent acts. The distinction matters because action changes the stakes.

Om Malik gets at the first important point this week. In The Copy and the Guru , he rejects the digital twin, as described by Reid Hoffman, as a substitute for the person.

A well trained model of your archive may be useful. It may help you remember, retrieve, and think. But a copy is not the person. Om puts it plainly: “The twin doesn’t just represent you. It restructures how others relate to you.” He adds, even more sharply, that “The copy becomes the relationship.” I agree that two minutes with the real Reid Hoffman is better than hours with a “twin.”

That is the trap the concept of Agentcy avoids. The goal is not to make a replica of the self and send it into the world as a substitute. The goal is to extend what a person or institution can do while preserving the fact that the agency remains human. A personal agent should not replace the relationship. It should help the human show up with more memory, more context, more leverage, and more time.

That is why the better metaphor is not the twin. Agents are your team.

The agents-as-team idea shows up across this week’s stories.

Dan Shipper argues that automation does not end work. It creates more of it, because humans keep discovering new things worth doing once execution gets cheaper. Boris Cherny , the creator of Claude Code, describes a world in which software engineering turns into a broader builder role. Designers, product managers, managers, and engineers can all increasingly use agents to make software. The work does not disappear. It moves up a level, toward judgment, direction, taste, customer understanding, and responsibility for what gets built.

Noah Smith gives the labor market version. His essay is titled Your future job will be to keep AI on task , and the subtitle is the important part: “knowing what we want.” That is the human advantage. Models can reason, write, search, code, and plan. Agents can execute multi-step work. But somebody has to know what good means. Somebody has to decide what the task is for. Somebody has to notice when a system is pursuing the wrong version of the goal.

This is not a sentimental claim about human uniqueness. It is an operational claim. If agents extend human agency, then the human role becomes clearer, not vaguer. Humans define purpose, set boundaries, grant permissions, inspect outcomes, and remain accountable for what agents do on their behalf.

Tomasz Tunguz names the software architecture underneath that shift. In Software After AI , he writes: “The end of the software era is the beginning of the harness era.” That is the sentence that explains why Agentcy is not just a branding exercise around better models. The model is only one part of the system. Around it sits context, memory, tools, orchestration, persistence, sandboxing, observability, governance, and cost control.

His clearest line is this: “Tools are how the agent affects the outside world.” That is the crux.

An agent with no tools is still mostly a conversational system. An agent with tools can take action. It can query a database, create a pull request, send a draft, search a file system, update a dashboard, route a task, call another agent, or ask for approval before doing something sensitive. Once tools enter the loop, the question is no longer only whether the model is correct. The question is whether the whole system is governed.

That is what has changed again. We are no longer only comparing answers. We are designing systems that act. And we are using them to build what is in our heads.

Anthropic’s Claude Opus 4.8 announcement is a useful marker. The company says users now have “control over the amount of effort Claude puts into a task.” It describes a model that is more reliable at agentic work, better at asking questions, better at catching mistakes, and more likely to flag uncertainty instead of claiming progress too soon. Claude Code’s dynamic workflows go further: Claude can plan work, run many parallel subagents, verify outputs, and report back.

That is Agentcy in product form. The model is not simply answering. It is working. It is being given effort settings, workflows, tools, permissions, memory, and verification loops. Anthropic even now allows developers to update system instructions mid-task, so budgets, permissions, and environment context can change while an agent runs. That is not a chatbot feature. It is infrastructure for delegated work.

The same pattern shows up outside coding. Gainsight is moving toward AI-native services, where agents help deliver outcomes that used to require people-heavy service teams. Compliance is becoming an AI market because regulated work is full of repetitive but judgment-sensitive tasks. Debt collection is becoming a warning sign because tireless agents can scale pressure against consumers. Military AI shows the same problem at higher stakes: humans may remain formally in the loop while systems compress the time available for judgment.

Agentcy is powerful because delegation is powerful. But delegation always raises the same questions. Who gave the instruction? What permissions were granted? What tools were exposed? What did the agent see? What did it do? Who inspected the output? Who is responsible when the action harms someone?

That is why Agentcy is not autonomy. It is also why pretending these systems are autonomous, and possibly dangerous in themselves, is foolhardy.

If an agent makes a mistake, the answer cannot be that the machine wanted something. The machine did not want. It had a role, a prompt, a policy, a workflow, a memory, a set of tools, and an environment. People and institutions built those things. People and institutions remain responsible for them.

Google shows the platform version of the same transition. Search used to be a referral layer. You typed a query, Google ranked the web, and traffic flowed outward.

With AI Overviews, AI Mode, agentic booking, shopping flows, and Gemini inside the product stack, Google is moving closer to becoming an action layer. It can answer, summarize, compare, recommend, and increasingly complete parts of the transaction. For publishers and businesses, this is not only an SEO problem. It is an agency problem. The distribution layer is acting on behalf of the user, inside its own walls, and everyone who depended on the click has to renegotiate their place in the system.

That is the broader pattern. Agents extend agency, but they do not extend it evenly. A founder with a well wired agent stack can move faster. An enterprise with governed data, approved tools, observability, and deployment teams can automate more deeply. A platform that controls the agent can absorb more of the value chain. A consumer with a weak assistant gets convenience, but may lose visibility into who shaped the answer or action. A worker supervising many agents may become more productive, or may become easier to measure, replace, and pressure.

This explains a lot about why engineering jobs are growing. Forward Deployed Engineers are required by AI companies to teach enterprises how to do this stuff.

The capital markets are already pricing the productivity possibility. Anthropic’s $65 billion Series H values the company at $965 billion post-money. The number is startling, but the explanation is more important. Anthropic says Claude is moving into core enterprise operations, that revenue run-rate crossed $47 billion, and that the company has lined up massive compute capacity from Amazon, Google, Broadcom, and SpaceX. That is the financial market putting a price on Agentcy: models, agents, workflows, compute, and enterprise dependency bundled into one company.

Elizabeth Warren’s proposal to tax AI belongs in this issue for the same reason. I am generally not a fan, but her argument is not only a tax argument. It is a surplus argument. If agents extend corporate agency, if they let companies do more with fewer people, more compute, more data centers, and more capital, then politics will ask who receives the gains. That debate is early, messy, and likely to be fought badly. But it is not going away. And it is not only an issue for politicians and regulators. It is a societal question we all have a stake in.

This week’s infrastructure stories make the same point in physical form. AI agents feel like software, but Agentcy runs on power, chips, networks, data centers, construction crews, permitting offices, and state capacity. Amazon’s network topology work, Packy McCormick’s data center optimism, and Arizona’s abundance playbook are all parts of the same story. Delegated digital work still needs a built world underneath it.

So this week’s claim is narrow but important.

The use case has evolved again.

AI is moving from answers to action. The agent is becoming a unit of work: directed by humans, equipped with tools, able to continue without constant instruction, and increasingly able to inspect and repair its own work. That does not make agents people. It does not give them moral agency. It does not remove human responsibility. This trend was clear for a few months, but it is now baked into actual deployments and new products.

It extends human agency.

That is Agentcy. Extension, not replacement. Delegation, not autonomy. The next question is not whether agents will act. They already do. The question is whose agency they extend, under what rules, with what tools, and with what accountability when the work is done.

Enjoy.