Attempting the Software manufacturing unit sample.

One of the attention-grabbing challenges of the AI ecosystem in 2026 is that new,
efficient patterns emerge quicker than I can undertake them. I’ll discover a handful,
get again to work, and understand a month later that I’d missed 4 or 5 extra.
The adoption cycle for Imprint this 12 months has been one thing like:

1. January: get each engineer onto Claude Code each single day
2. March: okay, let’s additionally get everybody else onto Claude Code or Claude Cowork each single day
3. April: native growth is bottlenecked on checkout and worktree mannequin,
   as a substitute create ~10 native workspaces which every have an impartial checkout of each repository,
   and function on the workspace degree, not on the repository degree, so it may well generate cross-repository
   pull requests throughout frontend, backend, development projects and knowledge monorepos
4. June: oh boy, agent-driven growth is closely constrained by lack of a typical job administration
   system with larger visibility and fewer permission complexity than Jira,
   so let’s migrate your complete firm over to Linear and arduous cease on Jira
5. July: yikes, now we’ve got visibility into all these tickets, lots of them are trivial
   however managing them by native growth isn’t scaling, let’s roll out an orchestrated harness
   which internally we name “Agent Fleet”,
   alongside the traces of Stripe’s Minions

The most up-to-date query for me has been determining learn how to undertake the software program manufacturing unit sample.
(After some mild analysis, the particular AI-context origin of this time period is barely messy to
attribute, however I believe it may be Justin McCarthy in February 2026’s
Software Factories And The Agentic Moment.)

The software program manufacturing unit sample is looping on a broad purpose, after which counting on the harness to
drive progress in direction of that purpose. Our first move at implementation is pretty primary:

1. An agent ability /linear-project-loop which reads in a Linear challenge and begins by auditing
   that challenge’s purpose definition on these dimensions:

   1. An RFC in Notion that describes the challenge’s objectives, how these objectives are measured, and the overall method
   2. A Datadog dashboard or Snowflake queries that measure progress in opposition to these objectives

   If these are lacking, or the Linear challenge is lacking in its entirety, it iterates with you on creating these lacking instruments.

2. Then it evaluations the state of the metrics and points for the challenge.
   If new work is recognized, it provides these points to the challenge.
   It updates the state of points which have moved.

3. It works on the non-blocked duties primarily based on the challenge’s present state. This is usually writing a pull request, updating a pull request, pinging for overview, asking
   a clarifying query, and many others.

4. When a job completes, if the challenge description is recent, it takes on the following job.
   If the outline hasn’t been up to date shortly, it reruns the loop beginning with step one.

Right now I’m operating this domestically in an area harness, but it surely’s working nicely sufficient that I anticipate
transferring the conduct to be pushed by the identical orchestrated harness that we assign one-off duties to.

What I significantly like concerning the manufacturing unit sample is that it parallels very intently how I’ve been working domestically,
whereas forcing me to acknowledge the locations the place I used to be by chance hording components of the state for myself relating to
the objectives of the challenge. I used to be already asking brokers to iterate on particular Linear initiatives, however they didn’t
have the power to judge in the event that they had been moving into the proper path, or if it was lacking needed duties.
Now it does.
The different place this has been extraordinarily useful for me is checking in on initiatives submit launch.
For instance, I shipped our passkeys implementation earlier this 12 months, however some months go by with out my checking in
on the way it’s going. If we noticed adoption spike, or error charges begin to flip, I’d miss it, however operating the
manufacturing unit in a much less frequent post-release mode would catch it instantly.

The ultimate thought that’s been attention-grabbing to me is how a lot all the items right here compound solely to the extent
that you’ve the opposite items. For instance, this manufacturing unit sample depends upon having Datadog MCP and Snowflake entry obtainable
to handle goal-tracking, but it surely additionally depends upon Linear being the only supply of state for the corporate’s work,
and an orchestrated harness that may carry out work independently out of your laptop computer. Keeping up with this
many migrations is an interesting business second.