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Generative AI can produce code faster than humans, and developers feel more productive with it integrated into their IDEs. That productivity is only real if CI/CD tests are solid and automated. When not appropriately tested, you may encounter a production issue that you haven’t seen before. According to the State of Software Delivery 2025 report, 67% of developers spend more time debugging and resolving security vulnerabilities in code generated by AI.

Extending Large Language Models (LLMs) with custom tools has become increasingly valuable in today’s AI landscape. Model Context Protocol (MCP) servers provide a standardized way to connect external tools and resources to LLMs. This can enhance their capabilities beyond basic text generation. While thousands of pre-built MCP servers exist, creating your own allows you to address specific workflows. You can implement use cases that off-the-shelf solutions cannot handle.

Retrieval-Augmented Generation (RAG) pipelines have become an integral part of how Large Language Models (LLMs) access information beyond their training cutoff. These pipelines enable LLMs to deliver current, accurate, and grounded responses. By fetching relevant external documents, RAG mitigates common LLM challenges like factual inaccuracies and hallucinations. However, this methodology introduces a new complexity: evaluating RAG pipeline performance is particularly challenging.

CI/CD pipelines are the heartbeat of modern software delivery. They automate builds, run unit tests, package applications, and deploy them to production with a speed that traditional release cycles could never match. For engineering teams under pressure to move fast, pipelines are the mechanism that makes agility possible.

For most teams, the slowest part of delivery isn’t writing code, it’s everything that happens after: automated tests, manual reviews, bug fixes, final approvals, and the long wait for deployment. The longer these phases run, the more expensive and painful late fixes become. As AI makes it easier to generate code at scale, those bottlenecks only get bigger.

An essential part of software development, testing helps catch bugs and errors early, improves software quality, and ultimately prevents costly issues from being deployed to production. The effectiveness of software testing will remain uncertain until it can be measured and that is where code coverage comes in. Code coverage is a metric that tells developers what portion of their codebase is executed when specific tests are run.

Any company still running their own CI/CD, such as Jenkins, is paying a price. The question is which one: Each of these is extremely costly, and yet many software teams still host and run their own CI/CD for two reasons: tools like Jenkins are cheap or free, and hosting Jenkins on an AWS EC2 instance is often cheaper per minute than SaaS CI/CD services like Bitbucket Pipelines. Both reasons are true, but they’re also a trap.

Today’s software organizations can’t make tradeoffs between speed and trust – you need both to succeed. But juggling them is tough. Moving too fast can lead to security vulnerabilities and compliance issues, while moving too slow means your competitors beat you to market. This tension creates friction that slows down every release, a problem that is rooted in your software pipeline.