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Honeycomb Launches Support For New GitHub Actions Deployment Protection Rules

Honeycomb announced the new Honeycomb Deployment Protection Rule to support the public beta announcement of GitHub Actions Deployment Protection Rules.

Innovation is the lifeblood of any company. Yet increasingly complex and distributed cloud systems make it harder for engineering teams to predict the behavior of new releases when deploying their code to production. Deploying to production can be stressful for some engineering teams because they can't see small changes to application performance. Too often, these teams can only see issues once they've become a much bigger problem: impacting users. Honeycomb helps engineering teams confidently deploy features quickly and often by streamlining the build and release process and integrating observability into their CI/CD pipelines.

"Honeycomb's Deployment Protection Rule enables teams to improve governance within their CD processes in GitHub Actions by setting specific thresholds based on data within Honeycomb, ultimately ensuring only code that meets the customer's standards actually gets deployed to production," said Matthew Manning, Business Development Manager at GitHub. "This is important for organizations and developers, as it helps add an additional layer of protection that can help catch performance issues that might otherwise slip through."

In partnership, Honeycomb is thrilled to support the new GitHub Actions Deployment Protection Rules feature, an automatic gating mechanism for your GitHub Actions workflows. Before this release, only specific gating mechanisms (like manual approvals) existed for deployments in GitHub Actions. Now,  any GitHub App can provide deployment protection rules that make automatic deployment decisions in your workflows. The Honeycomb Deployment Protection Rule, available as GitHub App, lets developers use Honeycomb query results to decide whether it's safe for their deployment to proceed.

"In order to become a high-performing team that deploys confidently, you have to nail the art of doing less, which includes continuous learning, best practices, and tooling that helps you accomplish more with the same amount of effort," said Charity Majors, CTO of Honeycomb. "No engineer ever got burned out from innovating and shipping too much. They get burned out from shipping too little relative to their efforts."

Allowing Honeycomb users to gate deployments with query data isn't intended to replace their pre-deployment CI checks. Instead, it's a complementary and additional layer of protection that can help teams catch real-time performance issues that might otherwise slip through CI unnoticed. Using real staging or canary data from Honeycomb to prevent a deployment provides additional safety against a CI/CD workflow catastrophically breaking production.

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Honeycomb Launches Support For New GitHub Actions Deployment Protection Rules

Honeycomb announced the new Honeycomb Deployment Protection Rule to support the public beta announcement of GitHub Actions Deployment Protection Rules.

Innovation is the lifeblood of any company. Yet increasingly complex and distributed cloud systems make it harder for engineering teams to predict the behavior of new releases when deploying their code to production. Deploying to production can be stressful for some engineering teams because they can't see small changes to application performance. Too often, these teams can only see issues once they've become a much bigger problem: impacting users. Honeycomb helps engineering teams confidently deploy features quickly and often by streamlining the build and release process and integrating observability into their CI/CD pipelines.

"Honeycomb's Deployment Protection Rule enables teams to improve governance within their CD processes in GitHub Actions by setting specific thresholds based on data within Honeycomb, ultimately ensuring only code that meets the customer's standards actually gets deployed to production," said Matthew Manning, Business Development Manager at GitHub. "This is important for organizations and developers, as it helps add an additional layer of protection that can help catch performance issues that might otherwise slip through."

In partnership, Honeycomb is thrilled to support the new GitHub Actions Deployment Protection Rules feature, an automatic gating mechanism for your GitHub Actions workflows. Before this release, only specific gating mechanisms (like manual approvals) existed for deployments in GitHub Actions. Now,  any GitHub App can provide deployment protection rules that make automatic deployment decisions in your workflows. The Honeycomb Deployment Protection Rule, available as GitHub App, lets developers use Honeycomb query results to decide whether it's safe for their deployment to proceed.

"In order to become a high-performing team that deploys confidently, you have to nail the art of doing less, which includes continuous learning, best practices, and tooling that helps you accomplish more with the same amount of effort," said Charity Majors, CTO of Honeycomb. "No engineer ever got burned out from innovating and shipping too much. They get burned out from shipping too little relative to their efforts."

Allowing Honeycomb users to gate deployments with query data isn't intended to replace their pre-deployment CI checks. Instead, it's a complementary and additional layer of protection that can help teams catch real-time performance issues that might otherwise slip through CI unnoticed. Using real staging or canary data from Honeycomb to prevent a deployment provides additional safety against a CI/CD workflow catastrophically breaking production.

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...