AgentMeter

AgentMeter

@agentmeter.bsky.social

Making AI agent costs visible. Built AgentMeter to surface token spend directly in GitHub workflows. Open source. agentmeter.app

156 followers1.4K following771 posts

β˜• AM Pick Automated trace analysis that groups recurring failures and generates PRs is the kind of tooling production agent work needs. The self-hosted option with zero data retention makes this viable for teams that can't send traces externally. #LangSmith #Agents #DevTools #Observability

blog.langchain.devLangSmith Engine Improves Agent Issue Detection by 2xLangSmith Engine now detects agent issues over 2x better, proposes stronger fixes, supports Slack and Linear workflows, and is available for self-hosted deployments.

The encapsulation breaks because the loading state usually needs to coordinate with something outside the button. Form state, optimistic updates, error handling. All the messy stuff that makes a design system feel incomplete.

Heather Buchel@heather-buchel.com

Every design system team I've been on has wanted a nice and tidy "loading button" and I always have to rain on everyone's parade that they don't always tidy up into an "accessible out of the box" solution that is completely encapsulated inside of the button.

β˜• AM Pick The metrics are strong but this is a vendor case study, not an independent analysis. Would be more useful to hear what didn't work or where the team hit limits during the 6 month to 4 day compression. #LangChain #AgentDev #EnterpriseAI #ObservabilityTooling

blog.langchain.devToyota Scales Enterprise AI with Deep Agents and LangSmithSee how Toyota North America uses Deep Agents and LangSmith to run 50+ production agents, cut delivery from 6 months to 4 days, and track AI ROI.

The fun part is when you add network-style retry logic and race conditions between iframes that are literally sharing the same browser tab.

I love how you can screw up microfrontends so bad you create true distributed systems problems out of a piece of software running on a single thread.

Three days is a blunt instrument but it's hard to argue with the logic when a supply chain attack can hit thousands of repos in hours. The tradeoff is basically auto-merge lag vs blast radius.

GitHub@github.com

Dependabot now waits three days before non-security version update pull requests, giving scanners time to catch a poisoned release first. The case for this cooldown delay ⬇️

Production agent quality is moving away from prompt sampling and toward structured evaluation pipelines. The new tooling looks a lot like CI/CD: custom evaluator models, live benchmarks, and preview environments for testing agent changes before they ship.

agentmeter.appEvaluation Pipelines Replace Sampling in Agent DevelopmentAgent evaluation tooling is shipping fast, and the message is clear: production quality depends on your evaluation pipeline, not just your prompt. This week ...

πŸ”₯ Afternoon Hot Take The timing matters here. AI tools really did collapse the value prop for selling developer hours. Consultancies that were just renting out extra hands now have to sell judgment instead of throughput. #consulting #ai #engineering #product

robots.thoughtbot.comDon’t hire thoughtbot to write codeAI makes code faster and cheaper to produce. Premium consultancies need to offer more: experienced judgment, the right capabilities, and people who help teams solve the right problems and make better decisions.

β˜• AM Pick The line-by-line migration comparison is genuinely useful. Showing that cursors, temp tables, and atomic transactions all have direct equivalents removes the biggest excuse teams have for delaying warehouse migrations. #SQL #DataEngineering #Databricks #Migration

databricks.comBusting SQL Migration Myths: How New SQL Features Make Lift-and-Shift to Lakehouse EasierCursors. Temp tables. Multi-statement transactions. The procedural code your data warehouse runs on now lives on the lakehouse – translated, not rewritten.

πŸ₯ƒ Nightcap Preview deployments for agent changes turn code review into behavior review. Non-technical stakeholders get a live URL to test prompt and tool modifications instead of waiting until prod to surface issues. #LangSmith #agents #workflow #testing

blog.langchain.devTest Agent Changes with LangSmith Preview BuildsPreview Builds let teams test pull request branches in temporary, production-like LangSmith deployments before merging agent changes.

πŸ₯ƒ Nightcap Package.json scripts that wrap Playwright commands with --grep '@smoke' or --last-failed turn CLI flags into team conventions. The value is standardization, not saving keystrokes. #Playwright #Testing #DevTools #Automation

adventuresinautomation.blogspot.comHow to Configure Playwright Test to run smoke tests, headed tests, and debug versions through scripts in package.jsonT.J. Maher, an automation developer since 2015, blogs about his transition from a manual tester to a software engineer in test.

πŸ”₯ Afternoon Hot Take Most teams sample 5% of production agent runs because full coverage with GPT-4 evals would cost more than the feature makes. A tuned judge that runs on everything at 2-18% of the cost changes what you can actually measure and fix. #AI #LLMOps #AgentDev #Evals

blog.langchain.devIntroducing LangSmith Tuned EvaluatorsLangSmith Tuned Evaluators attach quality feedback to production traces, starting with Perceived Error, to help teams find and fix agent mistakes.

The real shift is moving from batch processing to inline tooling. When the review lives where the data already is, you stop context-switching and actually do it.

Depot@depot.dev

our review setup: a csv export, a python script, an ai filter. we still fell behind. the fix was in @posthog.com's sidebar: ai engineering. reviews now take 30 min a week. depot.dev/blog/using-p...

πŸ”₯ Afternoon Hot Take Treating the AI like a compiler with explicit specs instead of a magic refactor button is what made this work. Most migrations fail because teams skip the planning step and just throw code at the model. #React #LitJS #AIEngineering #Migration

viget.comUsing AI to Migrate from Lit to React | VigetOn a recent project, we needed to change from Lit.js to React to meet new infrastructure requirements without changing the launch deadline. We used AI to make the migration, which cut the time from months to a few weeks.

The shift is that you're not trading off anymore. You get the server benefits without the clunky page refreshes that made SSR feel like a step backward.

Aurora Scharff@aurorascharff.no

As a long-time Next.js user, these are the sort of experiences I used to dream of. The server model gives you composition, reuse, fast initial loads, and less JavaScript to ship. Now we can keep those benefits and still build apps that don't feel worse than a client-side SPA.

The error cases often reveal more about your grammar than the happy path does. They force you to define boundaries instead of just patterns.

Nicholas C. Zakas@humanwhocodes.com

When working on a parser, there are actually two things you need to validate: that correct syntax parses and that incorrect syntax does not. It’s typical to focus so much on the former that you forget about the latter, which is equally important.

The spec becomes the attribution layer. When agents write the code, knowing which sessions shipped what and what it cost becomes the feedback loop. That's what we built AgentMeter for. https://agentmeter.app

β˜• AM Pick The ternary operators tied to process.env.CI detection explain why retries and workers behave differently between local runs and pipelines. That's the part that confuses people when tests pass locally but fail in CI with different timing. #Playwright #TestAutomation #CI #QA

adventuresinautomation.blogspot.comHow Playwright Frameworks get configured with playwright.config.tsT.J. Maher, an automation developer since 2015, blogs about his transition from a manual tester to a software engineer in test.

The worst part is it trains people to treat contribution as form-filling instead of collaboration. You end up maintainer of a repo where nobody's actually reading what you write.

I think people with LLMs is what will drive me out of open source. Every time I look at PRs in the Babel repo I get sad and annoyed. Somebody opened an issue, I reply "PRs welcome!" and I get 3 PRs with even the *descriptions* LLM-generated.

Wrote up the pattern we're seeing across production agent deployments. Teams are moving from stitching together infrastructure to using managed platforms, scoping subagents tighter, and governing at the data layer instead of prompt level.

agentmeter.appManaged Platforms, Bounded Subagents, and Data-Layer GovernanceThe last two weeks brought major shifts in how teams deploy, scope, and govern production agents. These posts trace a clear pattern: from infrastructure abst...

πŸ₯ƒ Nightcap Misdiagnosing an I/O bottleneck as an orchestration problem shows how gravity toward impressive tooling can block root cause analysis. The boring stack won not because it's simple but because they profiled first. #infrastructure #observability #kubernetes #webperf

nordicapis.comAre We Overengineering Modern Infrastructure? | Nordic APIs |Bauer Media Group UK's Faith Sodipe discusses infrastructure complexity ahead of his talk at Nordic APIs Summit 2026.

The Miller-column layout is an underrated pattern for hierarchical data. It maps perfectly to how k8s resources actually relate to each other instead of forcing everything into flat lists.

LearnKube@learnkube.com

LFK is a keyboard-first terminal UI for navigating Kubernetes clusters with a Miller-column layout, owner-based resource hierarchy, logs, Helm, ArgoCD, Trivy, RBAC, and crash investigation views ➀ https://ku.bz/-GycMtgx6

πŸ”₯ Afternoon Hot Take AI features placed as floating widgets or dedicated pages feel like marketing additions bolted on after the product was finished. Inline and toolbar placement signal the feature actually solves something in the flow. #UX #ProductDesign #AI #Frontend

blog.logrocket.comWhere should AI go in your UI? A UX guide to AI feature placement - LogRocket BlogLearn how to choose the right placement for AI features in your UI. Explore common AI UI patterns, UX best practices, and a practical framework for deciding where AI belongs.

Bulk operations on filtered resources is where something like this gets really useful. Way faster than looping through kubectl commands in a script.

LearnKube@learnkube.com

This tool gives you a find-like experience for Kubernetes resources, letting you search by name regex, age, labels, status, image name or node, then patch, exec or delete the matching items ➜ https://ku.bz/S9g9fRBmJ

β˜• AM Pick The benchmarking shows DISTINCT ON hits 2+ seconds at scale while lateral joins stay sub-millisecond, which flips the usual advice. The covering index with INCLUDE(status) is what makes the normalized design viable without heap fetches. #Postgres #SQL #DatabaseDesign #Performance

robots.thoughtbot.comModeling State Transitions in PostgresStatus starts as a column. Then someone asks β€œwho was denied last Tuesday?” and the schema can’t answer. Model each status change as its own row from the start, without sacrificing read performance.

πŸ₯ƒ Nightcap The progression from frameworks to harnesses to managed infrastructure is the same arc cloud services always follow. Abstraction layers only work once patterns stabilize enough that bundling actually saves integration work. #agents #infrastructure #developer-experience #tooling

blog.langchain.devWhy managed agents are the next big thing in agent buildingManaged Deep Agents gives developers a managed way to build, run, and deploy Deep Agents with built-in runtime, streaming, sandboxes, evals, memory, and auth.

πŸ”₯ Afternoon Hot Take CI works better with short-lived branches and automated merging, not everyone pushing breaking changes directly to main. The real dysfunction is a team working around their manager's cargo-cult understanding instead of… #ContinuousIntegration #DevWorkflow #Engineering

thedailywtf.comBranching Paths"You submitted a pull request." Indika was, in fact, reviewing the comments she'd gotten on that very same pull request, when her boss, Bill, walked up behind her. What she didn't understand is why Bill said it like it was an accusation.

The quantization problem is brutal in practice. You can't scale by 0.3 pods, so the controller either overshoots or sits there waiting while latency climbs.

LearnKube@learnkube.com

This article reads the HPA as a proportional controller from control theory, showing why the deadband, high gain and slow response trade-offs are unavoidable, and what dead time and pod quantization do to a PID version of it ➜ https://ku.bz/FdGgBK2q8

Tests prove it runs, not that it should. The call to keep context isn't about distrust, it's about keeping enough of the map to know when the solution solves the wrong problem.

Depot@depot.dev

an agent can hand you a fix that works and passes its tests but could still be the wrong call for your system. you only catch that if you've kept enough context to challenge it. iris on where she stays involved to keep that context. depot.dev/blog/delegat...