An AI teammate that builds itself: how we wired Avery into our company’s DNA | BuildForever Engineering<br>AI-first engineering / June 18, 2026<br>An AI teammate that builds itself: how we wired Avery into our company’s DNA<br>Avery is an AI engineering teammate wired into the systems and tools BuildForever's team uses every day — and it now writes nearly a third of the code the team ships to build Extra every week.

Last week, Avery wrote 31% of the code our five-engineer team shipped to production. But not just by being prompted - it identified the issues, diagnosed the root causes, generated the fixes, and carried them through to merged production code on its own. For the tiny team we are, that's a step-change in leverage: an always-on teammate turning bugs, alerts, and maintenance work into shipped code around the clock. What started as a side project is now woven into every part of BuildForever, and now lets anyone at the company ship code to production.

Avery lives in Slack, runs on its own Mac mini, and has access to the systems our team uses every day: GitHub for code, pull requests, and reviews; Linear for issues; email and WhatsApp for bug reports and community feedback; TestFlight and App Store Connect for crash reports and user reviews; Mixpanel for product analytics; and production logs in Cloud Logging to trace what actually happened.

Avery, in its own words.<br>Why it's different from a coding assistant

You talk to Avery in Slack. Mention it in a thread and it spins up a persistent agent session tied to that thread, so the conversation and the work behind it stay linked. Follow-ups resume the same session instead of starting over, it carries a durable memory across threads, and it runs scheduled jobs on its own without waiting to be asked. That persistence puts Avery in the same family as persistent agents like Claude Code or Devin.

But Avery is not just a code-completion plugin - what sets it apart is its proactivity, memory and context. It's wired into every system we actually run on:

It runs on real Mac hardware. Avery compiles and runs the Extra iOS app, logs into test accounts, and records the fix working. An agent on a Linux box can read Swift, but it can't run it or show you the result.

It debugs from production, not guesses. Avery pulls our production GCP logs, databases, analytics, and every bug channel together, so a vague report becomes a root cause backed by data.

It owns the whole PR lifecycle. From one Slack message, Avery spins up an isolated worktree, implements, tests, opens the PR, links the ticket, and tracks it back to the thread - then merges and cleans up when asked. Each task gets its own worktree, so several engineers can hand Avery work in parallel without stepping on each other.

It's versioned with the company. Avery's skills, scheduled jobs, and team conventions live in the repo next to the code. They improve through the same pull requests as everything else - and Avery can open those PRs against its own skills, reviewed like any other change. There is no no-code dashboard and no self-editing outside of review. Avery builds itself.

The next wave of AI engineering tools won't just live in the IDE. They'll be embedded in the company's DNA - the operating systems, tools, human conversations, logs, analytics, and rituals that turn code into product.

Avery proactively fixes bugs

From a single user report, Avery digs through production logs to find the root cause, opens a PR with the fix, runs the app on its Mac mini to verify it, and reports back in the same Slack thread.

Avery always does the first pass, freeing up the team to focus on other critical work.

Bug report about the Today tab to root cause, fix, verification, and a PR ready for review - all in one Slack thread.<br>Avery also set up automatic iOS crash reporting with BugSnag. A new crash shows up, Avery picks it up on its own, traces it to the root cause, opens a PR with the fix, and verifies it - without any engineer needing to be in the loop.

A new iOS crash is caught automatically via BugSnag - Avery traces it, fixes it, and verifies it on its own.<br>Avery also constantly watches our backend for trends in API errors. When a class of errors starts spiking, it alerts the team, traces the regression through the worker logs against a baseline, and puts up a fix - before it turns into a page.

Avery catches a 504 error spike, traces it through the worker logs, and opens a fix for review.<br>Fixing on the go

When we're away from our computers, Avery on mobile has been a game changer. Any bug we hit while using Extra, we can hand straight to Avery and fix it on the go - which has 10x'd how fast we turn around user-facing fixes against our SLAs.

Catch a bug while using Extra, hand it to Avery from your phone, and have a fix on the way - no laptop required.<br>Implementing new features

One of Avery's superpowers is implementing new features, with live verification through screenshots and videos. Here's Steven, co-founder...