Software Delivery Agent
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On-demand Webinar
On-demand Webinar

Enhance the software development life cycle with AI insights | Harness Resource | Harness

AI coding assistants have gained significant traction among software developers over the past few years. In fact, industry analyst firm Gartner has predicted that, by 2028, 75% of enterprise software engineers will be using them. But AI is transforming more than just code generation. Across the software development life cycle (SDLC), there's growing potential to use AI to reduce frustration, accelerate workflows, and improve quality far beyond the IDE.

During this thought-provoking Tech Talk, veteran technology journalist John K. Waters talks with industry expert Chinmay Gaikwad about how AI can reduce developer toil and drive efficiency in areas like code search, pipeline debugging, test creation, and vulnerability explanation. Attendees will gain insight into untapped AI opportunities across the SDLC—from build to deploy—and discover how tools like Harness are leveraging AI across 15 integrated products to streamline and secure the software delivery process.

Key Topics Include:

  • How AI is reducing friction in code repositories and accelerating onboarding
  • Using AI to debug CI/CD pipelines and interpret complex logs
  • Accelerating testing through dynamic test generation and relevance filtering
  • AI’s role in understanding and remediating security vulnerabilities
  • Verifying deployment quality and performance thresholds using intelligent automation

Whether you’re a developer, a DevOps engineer, or an engineering leader, this webinar will give you a fresh perspective on how to extend AI’s value beyond coding to a faster, smarter, and more resilient SDLC.

Published
January 1, 2024

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What you'll learn

Key Takeaways

Accelerate Onboarding with Natural Language Search

AI allows developers to query complex or legacy codebases using plain text. This drastically reduces the time it takes for new engineers to understand architecture and become productive.

Automate Pipeline Failure Resolution

Sifting through continuous integration logs is a manual and repetitive process. AI can automatically read these logs to identify the root cause of failures and generate tickets for rapid resolution.

Reduce Testing Time with AI Prioritization

By analyzing code changes and creating call graphs, AI can determine exactly which tests are impacted by new commits. This targeted approach can save organizations up to fifty percent of their testing time.

Human Oversight Remains Critical for AI

Despite advanced capabilities, human intervention is still required to validate business logic and ensure compliance with regulations like GDPR. Engineers are essential for verifying that AI functions properly in production environments.

Prioritize Data Privacy in AI Training

A major concern for organizations adopting AI is protecting proprietary data and maintaining regulatory compliance. Safe AI implementation relies on training models using industry best practices rather than sensitive customer information.