Software Delivery Agent
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On-demand Webinar
On-demand Webinar

How to rethink software releases in the age of AI | Harness Resource | Harness

Code output is scaling faster than delivery capacity, yet software teams are keeping the same deployment and review patterns, yet are surprised when release sizes creep up, reviews get noisier, and coordination costs climb. In our recently published State of AI-Driven Software Releases report, in association with Harness, we found a clear divide between organizations clinging to existing processes, guardrails, and tools to release AI-assisted and generated code, and those who recognize the need to change and experiment to avoid new bottlenecks emerging.

Watch this on-demand panel session to work out which camp you sit in, and the steps you can take to modernize your release processes to match AI speed.

You’ll learn:

  • How to rethink code review in a world of more and larger software releases
  • Why 57% still use “human-in-the-loop” review for every line of AI-generated code
  • The new generation of guardrails, and why only 49% have them in place
Published
March 31, 2026

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What you'll learn

Key Takeaways

Code Output Outpaces Delivery Capacity

AI tools are significantly increasing the volume of code produced by organizations. However, software release and deployment processes have not evolved quickly enough to keep up with this surge.

Progressive Delivery Mitigates AI Release Risks

Utilizing feature flags and progressive delivery principles is critical for safely deploying AI-generated code. This approach allows teams to quickly disable features if unintended consequences or security issues arise.

AI Can Automate Technical Debt Removal

Artificial intelligence can help manage the technical debt associated with progressive delivery. AI tools can identify unused feature flags and automatically generate pull requests to remove them from the codebase.

Engineers Must Maintain Code Accountability

Even when AI generates pull requests, human engineers must retain ownership of the code they ship. Code generation is only the first step, and the operational burden of maintaining that software remains a human responsibility.

Avoid Over-Processing the AI Transition

Traditional agile and scrum processes may struggle to accommodate AI agents in the development lifecycle. Engineering leaders should establish guardrails rather than rigid processes, as AI tooling is evolving too rapidly for perfect setups.