Craig Risi
Craig Risi at Beyond Market Intelligence is a file of 8 stories. The newest of them: “Scale Your Self-Hosted Runners with ARC's Latest Reliability Upgrades”, “Applying proven reliability metrics to measure whether AI agents truly perform”, and “AWS Smooths Event-Driven Growth with Unified EventBridge Custom Buses”. For teams managing their own CI infrastructure, ARC 0.15.0 brings meaningful improvements to how self-hosted runners operate on Kubernetes. Measuring AI agent reliability doesn't require new theory, just proven metrics. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every Craig Risi story on Beyond Market Intelligence, newest first.
Scale Your Self-Hosted Runners with ARC's Latest Reliability Upgrades
For teams managing their own CI infrastructure, ARC 0.15.0 brings meaningful improvements to how self-hosted runners operate on Kubernetes. The focus on reliability and observability means less time troubleshooting infrastructure and more time shipping code. This release respects that scaling compute shouldn't introduce complexity. For those exploring how AI infrastructure scales independently, our coverage of Tenstorrent's recent funding offers a complementary look at building trust in open-source foundations.
Applying proven reliability metrics to measure whether AI agents truly perform
Measuring AI agent reliability doesn't require new theory, just proven metrics. Grafana Labs proposes applying familiar reliability engineering principles to the unfamiliar challenge of determining whether an AI agent actually behaves well. It's a pragmatic move that treats AI agents like any other production system: test them, measure them, and hold them accountable. For teams already using Grafana Tempo 3.1 to redact traces with simple queries, this approach extends observability into AI performance.
AWS Smooths Event-Driven Growth with Unified EventBridge Custom Buses
Event-driven architectures scale fast, but the event infrastructure itself often doesn't keep up, especially when multiple teams or AWS accounts are involved. AWS's enhanced Custom Event Bus for Amazon EventBridge directly addresses this fragmentation, offering a unified path forward for growth. It's a confident step toward making distributed event systems feel less like a patchwork and more like a purposeful design. For another take on managing complexity at scale, see how EVE Online tames 20 years of telemetry data.
OpenTelemetry brings stable order to Kubernetes telemetry with processor milestone
Consistency in Kubernetes telemetry just got a welcome upgrade. OpenTelemetry has promoted its Kubernetes Attributes Processor to v1.0.0, bringing stable order to how observability pipelines handle cluster data. This milestone matters because unpredictable telemetry undermines trust in monitoring. A stable processor means teams can rely on what they see. For those already tracking the agent-focused future of cloud tools, Cloudflare's new CLI for AI agents offers a complementary look at where infrastructure management is heading.
Self-Service GPU Metrics Bring Team Clarity Without Cross-Team Exposure
Adobe engineers have shared an open-source approach that gives teams self-service access to their own Prometheus metrics in multi-tenant Kubernetes environments, without exposing other teams' data. That is clarity without compromise. For teams feeling constrained by shared infrastructure, this is a practical step toward autonomy. It's the kind of focused tooling that empowers teams to move faster. If you are exploring how AI agents handle memory, our article on open-source tools for lasting context may interest you next.
Navigating AWS Complexity with Smarter Terraform Infrastructure Management
The Terraform AWS Provider's v6.62.0 release lands as infrastructure grows more intricate, adding support for new capabilities while sharpening how Terraform reads existing resources. That dual focus, forward momentum and deeper comprehension, matters for teams juggling complexity. It is not about chasing novelty; it is about making the provider a more reliable partner in daily work. For a broader look at simplifying cloud workflows, our guide "Unlock ChatGPT for Work" pairs well with this momentum.
How Cloudflare uses AI to enforce engineering standards automatically
Cloudflare's latest move turns engineering standards from static documents into a live, AI-enforced control system across the software development lifecycle. That's a smart shift. Too often, best practices sit unread in a wiki, waiting for someone to stumble upon them. By embedding enforcement directly into the workflow, Cloudflare is making standards matter where the work actually happens. It's a practical, forward-looking approach that prioritizes consistency without slowing teams down.
Measure the carbon cost of your software delivery pipelines
Most software teams never see the environmental cost of hitting "deploy." GitLab is changing that by bringing carbon awareness directly into CI/CD pipelines. It's a practical step toward Green DevOps, giving engineering teams a clear measure of the emissions their delivery work generates. That visibility matters. You can't reduce what you don't track. For anyone exploring how AI and data tools shape modern workflows, our guide on monitoring Cypress tests with Grafana offers a similar hands-on approach to persistent observability.