Embark on a journey into the dynamic world of AI development with our blog series, where we explore the latest and most innovative AI Developer Tools. As we delve into the tools and technologies shaping the future of artificial intelligence, discover how these resources empower developers to create intelligent, efficient, and scalable solutions. Whether you’re a seasoned AI professional or just stepping into the realm of machine learning, these articles aim to provide insights, tips, and practical guidance to navigate the diverse landscape of AI Developer Tools. Join us in unraveling the potential and staying at the forefront of the ever-evolving field of AI development.
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AI and machine learning are increasingly part of DevOps tools from both startups and established vendors, transforming how DevOps teams operate.
AIOps software, already known for capabilities such as defect detection, code security scanning and access controls, is now complemented by a new crop of generative AI tools. These advancements could make contributing to DevOps processes easier and more accessible for IT and DevOps teams. By adding AI into their toolchains, IT teams can expect to increase efficiency and simplify mechanisms throughout the DevOps pipeline.
At its core, a DevOps toolchain is a set of integrated tools — often open source — that organizations use to design, build, test, manage and operate software. DevOps toolchains are foundational to CI/CD and automation.
Team collaboration is increasingly essential in DevOps as advanced observability and cloud cost optimization tools join the pipeline. Platforms such as GitLab, GitHub, Harness and OpsVerse are indicative of a broader shift to the cloud, enhancing support for remote and hybrid working models. These infrastructure changes and emerging technologies also create new opportunities for using data proactively.
AI tools can increase DevOps toolchain efficiency. Emerging security threats are driving the need for automated code scanning and vulnerability detection, and growing software supply chain security requirements have given rise to more security automation and improved analytics and monitoring. AI can also improve collaboration as more teams outside of development and operations, such as cybersecurity and finance, require access to pipeline data.
AI use cases in DevOps are starting to edge past the hype and into reality. However, as with so many emerging technologies, a pilot project or proof of concept is necessary before bringing AI tools into DevOps toolchains to ensure the tool meets requirements. Many tools offer demos so teams can practice using the tools with their organization’s infrastructure and data before implementation.
The current generation of AI tools targeting the DevOps toolchain address common tasks such as coding, collaboration and security. Here are some leading tools to consider (listed in alphabetical order).
OpsVerse, an emerging player in the DevOps market, bills Aiden as a copilot that uses generative AI to create and manage DevOps toolchains. Teams don’t have to purchase the entire OpsVerse managed DevOps platform to use Aiden — the software also integrates with other DevOps tools.
Aiden operates securely within corporate networks, safeguarding business-critical information. It learns continuously about infrastructure and app configurations, with the aim of delivering actionable insights that enable developers to detect and mitigate issues. Other notable features of Aiden include AI-guided CI/CD pipelines and a collaborative learning framework that draws on DevOps processes and interactions with internal developer teams.
Amazon CodeGuru is a static application security testing tool that uses machine learning and automated reasoning to identify vulnerabilities in code along with suggested remediations. CodeGuru includes two services:
Dynatrace offers comprehensive support for infrastructure and application observability, coupled with detailed analytics and automation for DevOps teams. Dynatrace’s Davis AI engine adds predictive analytics, automation and AI-driven recommendations to DevOps environments.
A key feature of Dynatrace’s Davis is its ability to provide natural-language explanations of system performance anomalies. This can significantly expedite problem resolution compared with presenting raw data that still requires interpretation and reporting. Additionally, these AI capabilities enable junior staff and less technical stakeholders to understand and interpret observability data without being observability experts.
Alongside its popular GitHub Copilot, GitHub recently launched three new AI features within GitHub Advanced Security for GitHub Enterprise Cloud and Enterprise Server customers. These enhancements include using large language models to identify leaked passwords, a capability now in public beta as part of GitHub’s secrets scanning feature.
Introducing AI to GitHub’s secrets scanning program makes it easier for teams to create custom patterns capable of searching for secrets specific to their organization. GitHub has also added AI to improve its alerting system and enhance its security overview dashboard.
JFrog Xray, a software composition analysis tool, integrates with Artifactory, JFrog’s repository manager. It uses AI to scan for potential vulnerabilities and license compliance issues in software components, including dependencies, helping DevOps teams manage risks in their software supply chain.
Other features of JFrog Xray include the following:
Kubiya is billed as an AI virtual assistant for DevOps, which developers can use within Slack or Microsoft Teams to interact with DevOps tools using natural-language requests. It’s a flexible tool that enables teams to participate in multiple conversations, run long jobs asynchronously and perform a variety of other tasks.
Kubiya can also define and generate DevOps workflows using generative AI. The workflow establishes guardrails within the DevOps toolchain, filtering and delivering only the options the DevOps team wants presented.
Kubiya can also answer questions based on internal documentation systems such as Notion and GitBook. Developers can give feedback on documentation accuracy, aiding in the creation and maintenance of technical documentation throughout the DevOps lifecycle.
Other notable Kubiya features include the following:
Editor’s note: Will Kelly selected these DevOps tools based on an analysis of AI’s growing role in the DevOps pipeline. His research included vendor demos, online user reviews and an assessment of vendor market share. This list is not ranked.
Will Kelly is a technology writer, content strategist and marketer. He has written extensively about the cloud, DevOps and enterprise mobility for industry publications and corporate clients and worked on teams introducing DevOps and cloud computing into commercial and public sector enterprises.
Part of: How AI and low-code development are shaping DevOps
Low-code and no-code tools expand the DevOps community in an organization and — when coupled with AI — can enhance the effectiveness of their workflows and products.
Low-code and no-code tools are often touted as solutions to the developer talent shortage — but they might not be the full answer for companies facing a lack of tech skills.
The DevOps toolchain is at an inflection point as enterprises seek to improve collaboration, productivity, efficiency and security across their DevOps lifecycle using AI.
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