Three agent bugs that turned out to be timeouts, type boundaries, and missing benchmarks.

I shipped a few things across our agent stack recently: a deadline-enforcement fix so server-side agents don't blow past their budget mid-round, a quieter bug where a [] versus undefined mismatch at a Java/TypeScript boundary was wiping stored artifacts on every continuation call, and a benchmark suite for the results-analysis agent so we can finally see where its latency budget is actually going. None of these were "agent problems." They were timeout handling, a type-system gotcha at a service boundary, and a measurement harness. The kind of thing you'd find in any distributed system.

I keep coming back to that. I think there's a real pull right now to treat agents like a category that exempts you from the basics, and I don't think it does. If anything I think agents make the basics more load-bearing, because when you're in a 30-iteration loop calling LLMs and tools, every weak point in your timeout, retry, and persistence story gets exercised. The artifact-clobber bug had been a slow leak everyone was living with. I think subtle issues like that are especially hard to surface on agent-shaped systems, because the LLM silently compensates around them for a while. The symptom looks like "the agent is a little off" instead of a hard failure, until eventually the compensation runs out. I think the answer is just normal robustness work to get reliable long-term behavior. Type-boundary tests, retry semantics, deadline plumbing. I'm spending a lot of time on those, and I don't think that's a phase.
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Claude and the mabl MCP now build my test scenarios, merge conflicts included

I use Claude with the mabl MCP to create mabl tests. A lot.

New CLI feature? Sure, I write the integration test. Then I also ask Claude to create a mabl test that proves the feature works for real.

This week I'm improving the flow merge page. To work on that page I need a merge conflict, and a merge conflict takes some setup. Create a test with a flow. Branch it. Edit the flow on the branch. Go back to master and change the same flow there. That's a lot of clicking just to reach the screen I want to fix.

So now I type "Claude, use mabl and do that for me!" My job is to fix the merge page, not to make merge conflicts by hand.

98% Improvement, or Bust

Time to optimize your cloud estate. "Ship it" is no longer the end of development.

Dust off the 12 optional optimizations you never shipped because they died in Jira. Feed the fallow tickets into the agent forge. Now is the time for the work we never had bandwidth to do.

Last week, I dusted off an old ML pipeline—long considered too complex to replumb. No problem: select the fanciest LLM model, crank it to MAX effort, and feed it some good ideas.

Flatten and optimize queries? Done.

Materialize all views? Done.

Cache expensive calls in Redis? Done.

Vectorize computations and push down to machine instructions? Done.

Build a comprehensive end-to-end validation harness and suite to ensure these changes break nothing? Check.

OK... 98% cheaper? Pencils down.

It's time to revisit everything you've ever shipped. Juice that lemon until it turns to diamond. Capacity and expertise are no longer the limit. With agent weights and your horse sense, 98% optimization is table stakes.

LFG.

Lean into the Discernment Horizon

Lean in. Today you can ask harder questions and produce more thorough analyses than ever before. You're the most capable you've ever been. Time to live your best coding life. Let's say you want to optimize your org's 100 CI workflows? You better instrument disk, memory, CPU, and network usage — OK... done. Now analyze the last 10K builds: what's the optimal build instance shape for _each individual_ workflow? Done. Now shadow-roll five custom instance shapes, raise dozens of PRs to consume them, and run 300 test builds. Done. Data? Reviewed. Statistical analysis, completed. Resources saved: 40%. Builds broken: zero.

You wouldn't have allocated the month it'd take to do this kind of work in 2024, but it's 2026 and your agent fleet just did it for you over the weekend.

Lean in. LFG.

Personal backlogs now churn fast, mostly deeper into what people already knew.

It's always been true that if you don't give a team enough scope, they'll fill whatever scope you do give them, usually further than you anticipated. I've watched that at the team level for years. What's new is watching it happen to individuals. The people who were already deep in devops are building more devops, and better devops. Same with the framework people, the networking people, the ones who care about UX polish. Everyone leans into what they already know. Personal backlogs are filling up and getting churned through faster than they used to. This is largely a very good thing. But it does create some interesting challenges around how we think about architecture, prioritization, and team commitments, and we're still working through those.

Deleting 400 stale i18n keys and the Bootstrap we'd carried for years

I've been deleting a lot of code lately and I cannot tell you how good it feels. I cleared out about 400 stale translation keys — the kind that accumulate over years and that nobody dares touch, because who still knows what references them? The agent knows; it checks every reference in seconds, so the sweep I'd always filed under "someday" turned into an afternoon. And Eugene and Jeff finally pulled out Bootstrap (BOOTSTRAP! the framework we've been trying to get rid of for years!) — thank you both, truly. I re-ran our UI library market share report afterward and that's when the impact really landed: Bootstrap is finally gone.

What surprised me is how the agents change the math. Migrating class components to function components with hooks, ripping out an old framework, pruning dead i18n — these were always "we should do this eventually" jobs that lost every prioritization fight. The cost of removing code and cleaning up our codebases dropped enough that now we just... do it. I keep catching myself opening diffs hoping they're mostly red.