Devoured - April 23, 2026
You're the Bread in the AI Sandwich (4 minute read)

You're the Bread in the AI Sandwich (4 minute read)

AI agentsengineeringworkflow Read original

A new engineering methodology positions humans as the planning and quality-control layers around AI execution, rather than competing with AI on implementation tasks.

What: Compound engineering is an AI-native framework that divides engineering work into four steps: plan, work, review, and compound. AI handles the execution middle layer while humans frame problems upfront and evaluate output quality afterward—the "bread" around the AI "filling."
Why it matters: This reframes the existential question facing knowledge workers: rather than asking whether AI will replace engineers, it proposes a division of labor where humans focus on problem diagnosis, taste, and judgment—areas where AI still struggles with generating multiple solution paths.
Takeaway: Consider whether your team should build individual AI assistants customized per worker or a single super-agent with shared departmental plugins—Every's experience suggests the latter scales better than originally expected.
Deep dive
  • The compound engineering framework separates engineering into four phases: plan (frame the problem), work (execute), review (judge output quality), and compound (iterate)
  • LLMs excel at execution—following steps and doing deep work for hours or days—but struggle with diagnosing problems from multiple angles like humans can
  • Humans provide the "bread" in the AI sandwich: framing on one end and taste/judgment on the other, with AI as the filling that does the heavy lifting
  • Every's AI agent "Claudie" started as a project manager but kept exceeding expectations, leading engineers to add capabilities as plugins rather than creating separate agents
  • Claudie now handles client updates, sales pipeline management, and slide deck creation for Every's consulting team, running on a Mac Mini with Claude Max
  • Each human consultant has a personal AI assistant for individual workflows, but they share Claudie for cross-team skills like deck building
  • Two organizational models will likely emerge: personalized assistants per employee (more customization, more maintenance) or company-wide super-agents with plugins (centralized maintenance, less flexibility)
  • The key human skill becomes judging whether AI output "feels right" and matches your vision—this separates meaningful work from generic output
  • Agents appear to have no ceiling on capabilities if you invest time building refined skills and plugins for them
  • The role of engineers shifts from pure execution to problem framing and quality control, which requires a different skill set than traditional coding
Decoder
  • Compound engineering: An AI-native engineering methodology where humans plan and review while AI executes the work
  • LLMs: Large language models like GPT-4 or Claude that can generate code, text, and other content
  • Agents: AI systems that can autonomously perform tasks over extended periods, often with access to tools and workflows
  • Plugins: Modular capabilities that can be added to an AI agent to expand what it can do, similar to apps on a phone
Original article

You're the Bread in the AI Sandwich

Plus: Trust batteries, and how many agents we'll have in the future

'AI & I': You're the Bread in the AI Sandwich

Today, we're releasing a new episode of our podcast AI & I. Dan Shipper sits down with Kieran Klaassen, GM of Cora and creator of Every's AI-native engineering methodology, compound engineering. Dan and Kieran discuss where humans fit now that AI can generate high-quality code, copy, strategy, and design. If the execution layer is largely solved, do engineers still have a role in the workplace?

The short answer: Yes. Think of an AI workflow like a sandwich—the model is the workhorse filling, and we're the bread, providing framing and taste.

Watch on X or YouTube, or listen on Spotify or Apple Podcasts. You can also read the transcript.

Here are the highlights:

  • Play to your strengths. Kieran's compound engineering framework breaks the engineering workflow into four steps: Plan, work, review, and compound. AI takes care of the doing phase. "LLMs are very good at just following steps, doing deep work, working for hours or days, even now," Kieran says. What's left for flesh-and-blood humans are the steps before and after—the planning, where you frame the problem, and review, where you determine whether the output feels right (the bread!).
  • Humans can identify multiple solutions to the same problem—AI struggles at this. If your knee hurts, you could take Advil, stretch your IT band, or stop running on hard surfaces. Humans are good at diagnosing a problem from many different angles, an exercise agents struggle with, Dan says.
  • Taste is the final layer of bread. Once AI has done the work, the most important thing you can do is judge whether the output approaches the vision in your head. Does the output feel right—and if not, how can you reframe the problem until the AI produces something that does? This is what separates art, which has a point of view, from generic slop.

Miss an episode? Catch up on Dan's recent conversations with LinkedIn cofounder Reid Hoffman; the team that built Claude Code, Cat Wu and Boris Cherny; Vercel cofounder Guillermo Rauch; podcaster Dwarkesh Patel; and others, and learn how they use AI to think, create, and relate.

Now, next, nixed

The agents are merging

Now: Claudie is an AI agent that runs on a Mac Mini with a Claude Max account. Since joining Every's consulting team a few months ago, she's been promoted multiple times and is now responsible for managing client updates, the sales pipeline, and the creation of slide decks.

Every engineer Nityesh Agarwal initially built Claudie as an AI project manager. The plan was to build separate agents to handle deck creation and the sales pipeline.

But every time he added a capability to Claudie's plate, she exceeded his expectations. And so instead of creating more agents, Nityesh converted their planned functionality into plugins within Claudie. "There doesn't appear to be any limit to how much this AI employee can do if you spend time building good, refined skills," he says.

Today, each (human) member of the consulting team has a personal AI assistant tailored to their own workflow, and they use Claudie to do tasks where they can take advantage of skills—such as slide deck building—that can be shared across the team.

Next: Two organizational architectures for agents will develop simultaneously, Dan predicts. In the first model, every person at a company gets their own AI assistant. In the second, workers across the organization will rely on a single super-agent with a library of department-specific plugins, similar to Claudie, but even bigger.

In the first case, each worker can customize their agent to their exact specifications, which allows for a richer relationship but requires setup and maintenance. In the second, one specialist does the upkeep of the agent and its plugins for the whole team or company, which takes the burden off each worker, but means they can't make any tweaks.