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When the creator of the world's most advanced coding agent speaks, Silicon Valley doesn't just listen — it takes notes.

For the past week, the engineering community has been dissecting a thread on X from Boris Cherny, the creator and head of Claude Code at Anthropic. What began as a casual sharing of his personal terminal setup has spiraled into a viral manifesto on the future of software development, with industry insiders calling it a watershed moment for the startup.

"If you're not reading the Claude Code best practices straight from its creator, you're behind as a programmer," wrote Jeff Tang, a prominent voice in the developer community. Kyle McNease, another industry observer, went further, declaring that with Cherny's "game-changing updates," Anthropic is "on fire," potentially facing "their ChatGPT moment."

The excitement stems from a paradox: Cherny's workflow is surprisingly simple, yet it allows a single human to operate with the output capacity of a small engineering department. As one user noted on X after implementing Cherny's setup, the experience "feels more like Starcraft" than traditional coding — a shift from typing syntax to commanding autonomous units.

Here is an analysis of the workflow that is reshaping how software gets built, straight from the architect himself.

How running five AI agents at once turns coding into a real-time strategy game

The most striking revelation from Cherny's disclosure is that he does not code in a linear fashion. In the traditional "inner loop" of development, a programmer writes a function, tests it, and moves to the next. Cherny, however, acts as a fleet commander.

"I run 5 Claudes in parallel in my terminal," Cherny wrote. "I number my tabs 1-5, and use system notifications to know when a Claude needs input."

By utilizing iTerm2 system notifications, Cherny effectively manages five simultaneous work streams. While one agent runs a test suite, another refactors a legacy module, and a third drafts documentation. He also runs "5-10 Claudes on claude.ai" in his browser, using a "teleport" command to hand off sessions between the web and his local machine.

This validates the "do more with less" strategy articulated by Anthropic President Daniela Amodei earlier this week. While competitors like OpenAI pursue trillion-dollar infrastructure build-outs, Anthropic is proving that superior orchestration of existing models can yield exponential productivity gains.

The counterintuitive case for choosing the slowest, smartest model

In a surprising move for an industry obsessed with latency, Cherny revealed that he exclusively uses Anthropic's heaviest, slowest model: Opus 4.5.

"I use Opus 4.5 with thinking for everything," Cherny explained. "It's the best coding model I've ever used, and even though it's bigger & slower than Sonnet, since you have to steer it less and it's better at tool use, it is almost always faster than using a smaller model in the end."

For enterprise technology leaders, this is a critical insight. The bottleneck in modern AI development isn't the generation speed of the token; it is the human time spent correcting the AI's mistakes. Cherny's workflow suggests that paying the "compute tax" for a smarter model upfront eliminates the "correction tax" later.

One shared file turns every AI mistake into a permanent lesson

Cherny also detailed how his team solves the problem of AI amnesia. Standard large language models do not "remember" a company's specific coding style or architectural decisions from one session to the next.

To address this, Cherny's team maintains a single file named CLAUDE.md in their git repository. "Anytime we see Claude do something incorrectly we add it to the CLAUDE.md, so Claude knows not to do it next time," he wrote.

This practice transforms the codebase into a self-correcting organism. When a human developer reviews a pull request and spots an error, they don't just fix the code; they tag the AI to update its own instructions. "Every mistake becomes a rule," noted Aakash Gupta, a product leader analyzing the thread. The longer the team works together, the smarter the agent becomes.

Slash commands and subagents automate the most tedious parts of development

The "vanilla" workflow one observer praised is powered by rigorous automation of repetitive tasks. Cherny uses slash commands — custom shortcuts checked into the project's repository — to handle complex operations with a single keystroke.

He highlighted a command called /commit-push-pr, which he invokes dozens of times daily. Instead of manually typing git commands, writing a commit message, and opening a pull request, the agent handles the bureaucracy of version control autonomously.

Cherny also deploys subagents — specialized AI personas — to handle specific phases of the development lifecycle. He uses a code-simplifier to clean up architecture after the main work is done and a verify-app agent to run end-to-end tests before anything ships.

Why verification loops are the real unlock for AI-generated code

If there is a single reason Claude Code has reportedly hit $1 billion in annual recurring revenue so quickly, it is likely the verification loop. The AI is not just a text generator; it is a tester.

"Claude tests every single change I land to claude.ai/code using the Claude Chrome extension," Cherny wrote. "It opens a browser, tests the UI, and iterates until the code works and the UX feels good."

He argues that giving the AI a way to verify its own work — whether through browser automation, running bash commands, or executing test suites — improves the quality of the final result by "2-3x." The agent doesn't just write code; it proves the code works.

What Cherny's workflow signals about the future of software engineering

The reaction to Cherny's thread suggests a pivotal shift in how developers think about their craft. For years, "AI coding" meant an autocomplete function in a text editor — a faster way to type. Cherny has demonstrated that it can now function as an operating system for labor itself.

"Read this if you're already an engineer… and want more power," Jeff Tang summarized on X.

The tools to multiply human output by a factor of five are already here. They require only a willingness to stop thinking of AI as an assistant and start treating it as a workforce. The programmers who make that mental leap first won't just be more productive. They'll be playing an entirely different game — and everyone else will still be typing.

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João Freitas is GM and VP of engineering for AI and automation at PagerDuty

As AI use continues to evolve in large organizations, leaders are increasingly seeking the next development that will yield major ROI. The latest wave of this ongoing trend is the adoption of AI agents. However, as with any new technology, organizations must ensure they adopt AI agents in a responsible way that allows them to facilitate both speed and security. 

More than half of organizations have already deployed AI agents to some extent, with more expecting to follow suit in the next two years. But many early adopters are now reevaluating their approach. Four-in-10 tech leaders regret not establishing a stronger governance foundation from the start, which suggests they adopted AI rapidly, but with margin to improve on policies, rules and best practices designed to ensure the responsible, ethical and legal development and use of AI.

As AI adoption accelerates, organizations must find the right balance between their exposure risk and the implementation of guardrails to ensure AI use is secure.

Where do AI agents create potential risks?

There are three principal areas of consideration for safer AI adoption.

The first is shadow AI, when employees use unauthorized AI tools without express permission, bypassing approved tools and processes. IT should create necessary processes for experimentation and innovation to introduce more efficient ways of working with AI. While shadow AI has existed as long as AI tools themselves, AI agent autonomy makes it easier for unsanctioned tools to operate outside the purview of IT, which can introduce fresh security risks.

Secondly, organizations must close gaps in AI ownership and accountability to prepare for incidents or processes gone wrong. The strength of AI agents lies in their autonomy. However, if agents act in unexpected ways, teams must be able to determine who is responsible for addressing any issues.

The third risk arises when there is a lack of explainability for actions AI agents have taken. AI agents are goal-oriented, but how they accomplish their goals can be unclear. AI agents must have explainable logic underlying their actions so that engineers can trace and, if needed, roll back actions that may cause issues with existing systems.

While none of these risks should delay adoption, they will help organizations better ensure their security.

The three guidelines for responsible AI agent adoption

Once organizations have identified the risks AI agents can pose, they must implement guidelines and guardrails to ensure safe usage. By following these three steps, organizations can minimize these risks.

1: Make human oversight the default 

AI agency continues to evolve at a fast pace. However, we still need human oversight when AI agents are given the  capacity to act, make decisions and pursue a goal that may impact key systems. A human should be in the loop by default, especially for business-critical use cases and systems. The teams that use AI must understand the actions it may take and where they may need to intervene. Start conservatively and, over time, increase the level of agency given to AI agents.

In conjunction, operations teams, engineers and security professionals must understand the role they play in supervising AI agents’ workflows. Each agent should be assigned a specific human owner for clearly defined oversight and accountability. Organizations must also allow any human to flag or override an AI agent’s behavior when an action has a negative outcome.

When considering tasks for AI agents, organizations should understand that, while traditional automation is good at handling repetitive, rule-based processes with structured data inputs, AI agents can handle much more complex tasks and adapt to new information in a more autonomous way. This makes them an appealing solution for all sorts of tasks. But as AI agents are deployed, organizations should control what actions the agents can take, particularly in the early stages of a project. Thus, teams working with AI agents should have approval paths in place for high-impact actions to ensure agent scope does not extend beyond expected use cases, minimizing risk to the wider system.

2: Bake in security 

The introduction of new tools should not expose a system to fresh security risks. 

Organizations should consider agentic platforms that comply with high security standards and are validated by enterprise-grade certifications such as SOC2, FedRAMP or equivalent. Further, AI agents should not be allowed free rein across an organization’s systems. At a minimum, the permissions and security scope of an AI agent must be aligned with the scope of the owner, and any tools added to the agent should not allow for extended permissions. Limiting AI agent access to a system based on their role will also ensure deployment runs smoothly. Keeping complete logs of every action taken by an AI agent can also help engineers understand what happened in the event of an incident and trace back the problem.

3: Make outputs explainable 

AI use in an organization must never be a black box. The reasoning behind any action must be illustrated so that any engineer who tries to access it can understand the context the agent used for decision-making and access the traces that led to those actions.

Inputs and outputs for every action should be logged and accessible. This will help organizations establish a firm overview of the logic underlying an AI agent’s actions, providing significant value in the event anything goes wrong.

Security underscores AI agents’ success

AI agents offer a huge opportunity for organizations to accelerate and improve their existing processes. However, if they do not prioritize security and strong governance, they could expose themselves to new risks.

As AI agents become more common, organizations must ensure they have systems in place to measure how they perform and the ability to take action when they create problems.

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