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A harness can stop unsafe code from shipping, but it cannot tell an agent that an integration already exists, which domain rule applies, or why an architecture decision was made. Context engineering makes that knowledge available when the agent needs it.

AI can increase the number of changes entering delivery. Architecture, security, and compliance review capacity does not automatically increase with it.

More approval gates will not solve that constraint. Teams need clear risk criteria, automated evidence, and accountable human decisions where the consequences warrant them.

You adopted spec-driven development. You write specs now, you’re careful, you also review them. You generate code from specs. You stopped doing “only” vibe coding.

It works better than before. But it’s still not… right.

Then you realize: you’re doing SDD without harness engineering. Specs aren’t enough. You need enforcement.

Vibe coding is what happens when you treat AI as a code typewriter instead of a partner.

You skip the specs. Skip the design thinking. Skip telling the AI what your architecture actually is. Then you’re shocked when:

• Hallucinations stack — The AI guessed once. Now that guess is “the pattern.”
• Knowledge vanishes — Nobody wrote down why. Good luck when the person who prompted it moves on.
• Quality has no guardrails — You’ll find the bugs after deployment, not before.

Agentic AI represents a paradigm shift in enterprise automation. Unlike traditional AI that responds to queries, agentic systems autonomously plan, execute multi-step tasks, use tools, and adapt to changing conditions while maintaining business alignment. This article introduces a comprehensive evaluation framework for assessing enterprise-grade agentic AI solutions.

Oh My Zsh is one of the most famous solutions for enhancing your terminal with additional features, plugins, and themes. After my last setup and configuration in 2017/2018, I decided it was time to update my entire configuration. I also took this opportunity to explore new plugins and themes that have become available.

Java and JVM performance monitoring and application profiling is essential not only for critical production workload. JDK Flight Recorder (JFR) and JDK Mission Control are the established solutions here. This article covers the Kubernetes/OpenShift solution Cryostat to bridge the gap to JVM applications in a Kubernetes runtime.

Secrets management in a cloud/Kubernetes and CI/CD context is crucial and should covers various aspect like

• how to maintain secrets in a secured way and avoid persistence in insecure repositories like git
• administrate access using a RBAC and enable segregation of duties
• life cycle management of secrets
• inject secrets automatically into application runtime

Existing solutions provides features to some of the mentioned aspects. One of the most widespread solution is HashiCorp Vault. HashiCorp Vault provides a solution to manage and protect secrets. Additional extensions and open source solutions interacts with HashiCorp Vault to inject secrets into an application context running in a Kubernetes/OpenShift runtime. This article explains some of the options and ends with a comparison.

Quarkus is one of the famous framework for modern Kubernetes native solutions. IBM Cloud Code Engine is the managed offering for serverless platforms. Let’s bring this together and build and run a Quarkus application with IBM Cloud Code Engine.

Docker announced the license and subscription plan changes for the Desktop Docker apps. Now at the latest, one can look for alternatives that have been around for a while. This article covers the replacement of Desktop Docker with podman on macOS.