Compound Engineering

January 30, 2026

What happens when 100% of your code is written by agents? Every (the AI media company) coined Compound Engineering to describe their answer: a workflow where each feature makes the next one easier to build, not harder.

The core insight: traditional engineering expects complexity to compound against you. Compound engineering flips this—each bug fixed, each lesson learned gets fed back into the system, making agents smarter over time.

The Loop

Compound engineering runs in four steps:

1. PlanAgents research the codebase, read commit history, scour the internet for best practices, then write detailed implementation plans

2. WorkAgents write code and tests according to the plan. Use MCP tools (Playwright, etc.) so agents can interact with the app as users

3. AssessReview with linters, tests, and parallel subagents checking security, performance, complexity from different angles

4. CompoundRecord lessons learned—bugs, patterns, solutions—so agents use them next time. This is where the magic happens

The time split is roughly 80% plan/assess, 20% work/compound. Planning is where developers spend most of their energy now.

Why It Works

"Today, if your AI is used right, a single developer can do the work of five developers a few years ago."

— Every engineering team

The compounding happens because lessons get written down as prompts that live inside your codebase. A new hire who's never seen the codebase before is as well-armed to avoid common mistakes as someone who's been on the team for years.

Every runs five software products in-house, each primarily built by a single person, used by thousands daily. Not demos—production systems.

The Compound Step in Practice

After a code review, you tell the agent: "Look at the comments, summarize them, and store them for later."

For example, in Every's AI email assistant Cora, before building anything new, the agent asks itself:

Where does this belong in the system?
Should it be added to something existing or need its own thing?
Have we solved a similar problem before?

These questions come with specific technical examples from past mistakes, priming the agent to find the right solution.

What Changes

With compound engineering, some traditional practices become unnecessary:

Manually writing tests — agents generate them
Extensive documentation — agents read and understand code directly
Weeks for new hires to commit — they inherit all the compounded learnings
Legacy lock-in — replatforming becomes feasible when agents can understand and migrate old code

Tools

Every uses Claude Code primarily, but the workflow is tool-agnostic. Some use Factory's Droid or OpenAI's Codex CLI.

They've open-sourced a compound engineering plugin for Claude Code that runs their exact workflow—including the 12 parallel subagents for code review.

The Flywheel

As one developer put it:

The key point is the compound step. Each time you build something, the agent learns. The next thing gets easier because the agent knows more. You never solve the same problem twice.

This is the core mental shift: stop thinking about code as something you write, start thinking about knowledge that accumulates.

Sources

Compound Engineering: How Every Codes With Agents — Every
Teach Your AI to Think Like a Senior Engineer — Every
compound-engineering-plugin — Every (GitHub)