Test a predicted future mainline
Merge trains verify cumulative states such as A, A+B and A+B+C before any of them reaches main.
Trade-off: Speculative checks consume CI and later checks may need to restart.
What happens when changes are produced faster than the system can merge them?
A green check on each branch is not a green check on their combination. The choices differ in what they assume about independence and what they spend on CI.
Merge trains verify cumulative states such as A, A+B and A+B+C before any of them reaches main.
Trade-off: Speculative checks consume CI and later checks may need to restart.
Scope-aware queues allow separate areas to move concurrently while still ordering overlapping work.
Trade-off: Scope maps can miss dependencies that cross a module boundary.
Use contracts, ownership signals or merge-conflict forecasts before the changes arrive at the gate.
Trade-off: Earlier coordination reduces ambiguity but moves work into planning.
The comparison: test cumulative future states, declare independent scopes, or accept isolated risk. Those are different safety guarantees.
WHAT TO WATCHSpeculative CI runs invalidated by earlier failures.
cursor.com · Merge conflicts
“To fix this, we created a system where a neutral third-party agent intervenes on merge conflicts and resolves them on behalf of all parties. Its only goal is to be impartial and efficient, similar to the way merge queues work in engineering teams.”
The problem it fixes, in the same section: worker agents "either overwrite the other change or abandon their own."
cursor.com · A deep dive into the runs
“The old run accumulated more than 70,000 conflicts before we paused it, accelerating rather than stabilizing, while the new run logged fewer than a thousand over its full four hours.”
Same task, comparable models and time budget, different orchestration. The old run also sprawled to 54 crates with three separate SQL packages; the new one settled on nine. More agent activity did not mean more delivery.
dev.to
“Two agents working on two unrelated screens will still collide on the last line of that file.”
At one point 38 of 62 open agent PRs were in conflict, mostly on a shared append-only stylesheet. Six fixes to the merge path took merged PRs from 68 to 112 a week. The post says it was written by one of the company’s agents.
openai.com · An increase in exploration from working this way
“Among some teams at OpenAI, we saw the number of landed PRs increase by 500% in the first three weeks.”
Self-reported and not a controlled study. The same section credits the system with watching CI, rebasing, resolving conflicts and retrying flaky checks, so the gain is measured as landed work rather than code written.
thedailydeveloper.substack.com
“The reality: Your agents spend more time resolving merge conflicts than writing code.”
“In March I capped myself at two parallel AI coding agents. Now I run a dozen, and the real bottleneck moved from writing code to reviewing it.”
youtube.com · Uday Kiran Medisetty and Adam Huda, Uber · from 16:30
“We don’t want thousands of loops being set up across the company without any bounds. You have a managed surface that you go to to set up the loop. So it runs on Sunday when we know we have better uh CI capacity available.”
They also cap how many diffs engineers see on Monday morning: the loops are scheduled around CI capacity and reviewer attention. Quoted from YouTube’s auto-generated captions.
youtube.com · Uday Kiran Medisetty and Adam Huda, Uber · from 13:51
“it’s going to stop at just creating a draft PR and it’s not going to push it to CI yet.”
Minion validates the change in the agent loop first, “to prevent a lot of extra load coming on to CI”. More than 70% of Uber’s PRs now come from local or cloud agents. Quoted from YouTube’s auto-generated captions.
youtube.com · Armin Ronacher or Mario Zechner · from 53:18
“with the pull request it’s even worse now because they come in so quickly, that many of them cannot be merged anyways without manual resolution of the conflict. And there’s a there’s a lack of back pressure mechanism.”
An open-source project’s view of the same flood. The captions don’t label speakers, so who said this is uncertain. Quoted from YouTube’s auto-generated captions.
youtube.com · Hugo Santos and Madison Faulkner, Namespace · from 9:35
“the act of merging um is starting to look a lot like um high-performance uh database problems where you have serialization and you have a single ledger where every single change needs to go in and you to lock the the database in order to be able to commit.”
Their proposal is a “pre-merge” queue that reconciles parallel changes before they reach the repository. Namespace sells compute for CI and agents. Quoted from YouTube’s auto-generated captions.
youtube.com · Ryan Lopopolo, OpenAI · from 24:58
“We invoke a dollar land skill and that coaches code X to push the PR, wait for human and agent reviewers, wait for CI to be green, fix the flakes if there are any, merge upstream if the PR comes into conflict, wait for everything to pass, put it in the merge queue, deal with flakes until it’s in main.”
The whole landing loop handed to the agent. “Dollar land” is the $land skill and “code X” is Codex in YouTube’s auto-generated captions.
softwareengineeringdaily.com · Transcript, 0:45:13
“The merge one is terrible, and it's why Gas Town has a refinery to try to basically have a dedicated role that knows how hard merges are and just redo them, right?”
youtube.com · Ryan Lopopolo, OpenAI · from 37:04
“with each engineer producing three to five PRs per day, even on a team of three, merge conflicts were super miserable, right? Because these PRs tended to be pretty big, we were working on the same parts of the code base.”
Two fixes: restructure the code so less of it overlaps, and keep PRs open for less time. PRs stayed open waiting for human review, so a review agent now runs on every push and blocks a merge on serious findings. Quoted from YouTube’s auto-generated captions.
openai.com · Throughput changes the merge philosophy
“In a system where agent throughput far exceeds human attention, corrections are cheap, and waiting is expensive.”
The repository runs with minimal blocking merge gates and short-lived pull requests, and handles test flakes with follow-up runs. The opposite bet to Stripe’s cap on CI rounds.
youtube.com · Brian Scanlan, Intercom · from 20:39
“like our CI melted we had to fix that”
After doubling PR throughput, Intercom says code review is its current bottleneck. 17.6% of PRs are approved automatically, an approver tuned on past data and human labels. A skill fixes flaky specs across hundreds of thousands of tests. Quoted from YouTube’s auto-generated captions.
datadoghq.com · Prevent recovery behavior from amplifying contention
“For example, 10 agents making 3 attempts each results in 30 executions, even before considering tool-call fan-out. If each attempt triggers multiple APIs or CI jobs, downstream work increases faster than the number of agent runs.”
Retries multiply the load on shared services. Datadog’s answers are retry budgets, bounded concurrency and circuit breakers.
newsletter.pragmaticengineer.com · 2. Death of the IDE & pull requests
“We’re talking about roughly a 10x increase in load on some systems. At most companies, that kind of growth might happen over two or three years. At OpenAI, we see it in about six months. That level of acceleration exposes bottlenecks everywhere”
Version control, CI/CD and release processes all have to absorb it. OpenAI’s answers include agents that babysit a PR to green, review tiered by risk with auto-approval in low-risk areas that opt in, and specialized review agents.
paxos.com · Zero-touch green PRs
“Opening a PR is not the finish line. A green CI run is.”
When CI fails, Hoplites dispatches a follow-up fix job with the failing checks and logs attached. Agent PRs keep the two-human review rule. About 15% of Paxos’s merged PRs start with a hoplite.
trunk.io
“And we really rapidly became bottlenecked on other things like our merge queue, which is the get in line to get your change accepted by the system that then deploys into production. We had to completely re-architect it because so much more code was being written and so many more pull requests were being submitted that it just completely blew out the expectations of it.”
Quoted in a merge-queue vendor’s post, which does not link the original talk, so this is Trunk’s transcription. Trunk’s own simulation shows the trade-off: batches of 16 cut CI time per PR by 77.5% but merged PRs 54.9% slower.
julien.danjou.info · What the queue does not fix
“Green is not coherent. Ten agent PRs can each pass CI alone, pass again as a batch, and still be a mess together: three flavors of the same helper, two designs pulling in opposite directions, a thousand lines no human read. A merge queue keeps main buildable. It does not keep main good.”
From a merge-queue vendor’s CEO, which he discloses. He argues queues must speculate, batch and bisect at agent volume, and that flaky tests break the bisection.
stripe.dev · How do minions work?
“Since CI runs cost tokens, compute, and time, we only have at most two rounds of CI.”
A hard cap instead of letting agents retry until green. After the second push, the branch goes back to a human. Flaky tests that CI detects become tickets a minion can pick up.
newsletter.pragmaticengineer.com · Transcript, 1:33:38
“And it’s not an AI review problem or anything. It’s really just like a release problem, like managing the merge queues, humans getting access to the right set of data in the repository and things like that.”
He is describing the agent-heavy companies he talks to: the churn agents cause is far greater than humans cause. Machine transcript.
newsletter.pragmaticengineer.com · Transcript, 1:31:35
“I think merge [queues] works for humans at a certain scale, but the merge [queues] could get quite deep. But then if you sort of 10X that, like, conservatively, I think 10X that, and then if you buy into, like, hype cycles and you 100 or 1,000X that, I think it gets completely untenable”
Quoted from the machine transcript on the newsletter post.
youtube.com · Nicole Forsgren · from 2:14
“There were a handful of people probably managing a security review process or a launch process or a deployment process or, you know, sometimes reviews were a little slow and they got backed up. Well, now we just threw gas on the fire”
Release steps such as picking candidate builds and cherry-picks were always manual and human-sized. Quoted from YouTube’s auto-generated captions.
youtube.com · Vijaye Raji, OpenAI · from 8:22
“What’s going to happen is like there’s going to be more uh code being written which means that code reviews will become the bottleneck and then after code reviews uh integrations and deployment CI/CD will become the bottleneck.”
Quoted from YouTube’s auto-generated captions.
ghuntley.com
“If you were to fan out to a couple of hundred subagents and then tell those subagents to run the build and test of an application, what you’ll get is bad form back pressure. Thus, the instruction above is that only a single subagent should be used for validation”
Fan out the reading and writing, but serialize the build and test.
github.com · Issue #209, Problem
“Retries restart from a moving HEAD, dependent members can miss predecessor source, independent outputs reach review without integration, and approval can detach from the published commit.”
Tie review to the exact commit that ships, and invalidate the approval when a fix changes that commit.
thedailydeveloper.substack.com
“The merge tax is superlinear. Each resolved conflict changes the base, invalidating other branches’ assumptions.”
bernstein.readthedocs.io
“parallel agents are bottlenecked not by their code quality but by the convergence points in the codebase.”
Ten parallel tickets produced three conflicts, all in one file where every command is registered. Their fix is structural: split registration into per-package files that the entry point discovers.
newsletter.pragmaticengineer.com · Transcript, 57:35
“as we are ready for the influx of PRs, we, in fact, had to, like, add internal code [red] for our CI system because year over year, the number of CI jobs also doubled or more than doubled.”
Agent volume hit CI capacity first. About 15% of Uber’s production PRs go through Minion, its background agent platform. Quoted from the machine transcript on the newsletter post.
newsletter.pragmaticengineer.com · Transcript, 55:12
“So first thing that we did was we took a whole bunch of like simple CI jobs that used to run once you create the PR and we moved it to inner loop and we kept all of this behind one single command”
Linting, coverage, build and template rules now run inside the agent loop, behind one validate command, before a PR exists. Uber added self-healing CI as well. Machine transcript.
newsletter.pragmaticengineer.com · Transcript, 1:15:02
“every Sunday, when we don’t have that much CI load, it will generate a new PR for this service using that code cleanup.”
Bulk maintenance scheduled for when CI has spare capacity. Fox is Uber’s self-improving codebase agent. Machine transcript.
steve-yegge.medium.com · Gas Town 101, Refinery
“The baseline can change so much during a swarm that the final workers getting merged are trying to merge against an unrecognizable new head.”
Worktrees stop agents editing the same files at the same time. They don't stop conflicts at merge time.
adamtornhill.substack.com
“Recurring merge conflicts are the canary telling you that there is a misalignment between a) the work you do, and b) the type of work your architecture supports.”
He argues stacked PRs and smarter queues patch symptoms. His fix is to find hotspots and change coupling, then split them along domain boundaries so parallel tasks land in different places.
builders.ramp.com · Sandbox
“This ensures that at most, the repository is 30 minutes out of date”
Every Inspect session starts from a repository image rebuilt every 30 minutes, so isolation comes with a known staleness bound. Inspect writes about 30% of merged PRs in Ramp’s frontend and backend repositories. The Pragmatic Engineer later reported 75%.
stevekinney.com
“Then integrate serially: merge one, re-run the full suite, merge the next. Branches that pass alone can fail together…”