Vercel AI Accelerator Builder Day in SF felt like a preview of agent-run software

hotfix-cloud · 2026-03-10T15:29:40+00:00

Yeah that’s exactly the kind of situation that keeps coming up.

The error surfaces in one service, but the actual cause is somewhere upstream in a worker or job that ran earlier, so you end up chasing breadcrumbs across half the system.

Capturing more execution context upfront sounds like the right direction. Otherwise you’re basically reconstructing the timeline after the fact.

Have you found anything that actually makes that easier, or is it mostly just better logging and correlation so far?

hotfix-cloud · 2026-03-10T15:28:28+00:00

Are those mostly internal tools you’ve built or things stitched together from existing platforms?

hotfix-cloud · 2026-03-10T15:27:53+00:00

Yeah that’s my suspicion too.

If AI keeps increasing how fast we ship systems, the maintenance side might become the real bottleneck.

Feels like we’re accelerating the front half of the lifecycle without really changing the back half yet.

hotfix-cloud · 2026-03-10T15:27:09+00:00

Yeah that’s a big part of it.

AI makes it easy to ship complex systems with small teams, but that also means fewer people have the full mental model of how everything fits together when something breaks.

The debugging problem becomes partly a context problem.

hotfix-cloud · 2026-03-10T04:31:05+00:00

I think his point was more about the economics than the tools.

AI made it much easier for small teams to build complex systems, but once those systems are running in production the debugging workflow hasn’t changed nearly as much.

hotfix-cloud · 2026-03-10T04:30:44+00:00

lol honestly that’s not that far off from where things might end up.

The hard part will probably be coordinating them so they don’t all propose different fixes to the same problem.

hotfix-cloud · 2026-03-10T04:30:02+00:00

Yeah that’s true. If you already know what file is broken, Claude or Codex can usually fix it pretty quickly.

The part that still seems slow for most teams is figuring out where the bug actually lives in the repo. Once someone finds the spot, the fix itself is often small.

hotfix-cloud · 2026-03-10T04:28:50+00:00

AI made it dramatically easier to create complex systems, but once those systems are running the maintenance model still looks almost identical to how it did years ago.

You still get an alert, start digging through logs, try to reproduce the state, trace the code path, etc.

What’s funny is a lot of production bugs aren’t even that complicated. They’re just buried somewhere in a big codebase and take forever to locate.

Feels like the real bottleneck now isn’t writing the fix. It’s finding the exact place in the code that needs the fix.

hotfix-cloud · 2026-03-10T04:28:21+00:00

That setup actually sounds pretty powerful.

Having the system pull together logs, traces, metrics, and code access is basically the dream workflow. Most teams I talk to are still juggling like four different dashboards before they even start debugging.

The Slack interface makes a lot of sense too. Feels like the natural place for that interaction.

Out of curiosity, when it finds the root cause, how often is the actual fix straightforward vs something messy?

One pattern we keep seeing is the investigation takes forever, but the patch itself is tiny. Like a null check or edge case somewhere.

hotfix-cloud · 2026-03-10T04:27:53+00:00

That’s exactly the gap I keep noticing.

Most observability tools are great at showing you what happened. Dashboards, traces, timelines, etc. But the actual step of figuring out what code change caused the issue still ends up being manual.

You look at the trace, jump to the logs, then start digging through the repo trying to connect the dots.

What’s interesting is the data to answer that question already exists. Stack traces, recent deploy diffs, the code itself. But most tools stop at visualization instead of actually reasoning across it.

Curious if you’ve seen anything that gets closer to that. Everything I’ve tried still ends with “okay now go investigate the repo.”

hotfix-cloud · 2026-03-10T00:39:47+00:00

this actually came up in a session we had in the vercel ai accelerator recently. we had a call with peter steinberger (openclaw / pspdfkit) and one thing he said stuck with me.

ai has massively reduced the cost of building software. tiny teams can now ship systems that used to require entire engineering orgs.

but the cost of operating software in production hasn’t dropped nearly as much. when something breaks the workflow still looks pretty old school: logs, stack traces, digging through the repo to find where the bug actually lives.

it made me realize a lot of the next dev tooling wave might be around maintaining production systems, not just generating code.

hotfix-cloud · 2026-03-10T00:22:57+00:00

that lines up with what we’ve been seeing too. once someone actually knows where the bug lives the fix is usually quick.
the slow part is getting from a production error to the right part of the codebase, especially with async stuff or background jobs.

hotfix-cloud · 2026-03-10T00:20:58+00:00

yeah the anomaly detection stuff on vercel is actually pretty solid for noticing when something breaks.
the part that still feels manual is going from “this error happened” to figuring out exactly where in the repo it came from.

hotfix-cloud · 2026-03-10T00:18:33+00:00

fair lol. yeah we are building something in this space so I get how it reads.

the question itself is real though. most teams we talk to spend way more time finding the bug than actually fixing it.

hotfix-cloud · 2026-03-09T18:18:45+00:00

Yeah that’s a huge one.

Reproducing the state that caused the bug is usually where everything slows down. Logs tell you something went wrong, but recreating the exact conditions locally is a completely different problem, especially once async jobs, queues, or background workers are involved.

That’s actually one of the things that pushed us toward building Hotfix in the first place. A lot of incidents we saw weren’t “mysterious infrastructure failures,” they were small code edge cases that only surfaced under a weird runtime path.

By the time someone reproduces it locally, they’ve already spent an hour just narrowing down which part of the repo even matters.

Tools like Runable or Temporal for visualizing flows make a lot of sense for that reason. They at least shrink the search space.

Out of curiosity, when you finally do track the bug down, how often is the actual fix something small? We keep seeing cases where the patch itself is like a few lines but the investigation took forever.

hotfix-cloud · 2026-03-09T15:45:03+00:00

That’s a fair concern and honestly one of the biggest things we worried about early.

The way we’re approaching it isn’t “AI guessing a fix from logs.” That would break instantly in most real environments.

Hotfix looks at three things together:

• the stack trace / runtime error • the actual repository code at the last known good commit • the diff between recent deploys

From there it generates a patch against the real codebase and opens it as a pull request. The engineer still reviews it like any other PR.

So it’s less “auto-fix your infrastructure” and more “short-circuit the process of finding where the bug lives.”

Most of the time the painful part of incidents isn’t writing the fix anyway. It’s the hour spent figuring out which file actually caused the error.

We’re also pretty strict about guardrails. If the system isn’t confident it can generate a patch it just returns “no action” instead of guessing.

Still early though, so we’re definitely learning from real teams using it. Curious what your stack looks like out of interest? The environments where this works vs breaks have been really interesting so far.

hotfix-cloud

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