Software Delivery Agent
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On-demand Webinar
On-demand Webinar

Why AI necessitates a platform model | Harness Resource | Harness

The introduction of AI means there are more entities involved in the creation of code than ever before. While code volume has largely increased as a result, we quickly learned that writing code was never the bottleneck to improved delivery, and the speed of AI is just creating more risks and hurdles further along the release pipeline.The 2025 DORA report highlighted internal developer platforms as the best foundation for effective AI adoption, allowing for the creation of guardrails and gates that suit your organization’s risk profile. Further, a high-quality platform provides rapid feedback and amplifies the effects of AI adoption on organizational performance.Watch this expert panel — with DORA lead Nathen Harvey, Thoughtworks CTO Rachel Laycock, and Harness Field CTO Martin Reynolds — to learn how to:Establish a platform that becomes the governance layer for scalable, cross-organizational data integrationBuild ‘Golden Paths’ to encourage AI-powered experimentation and ease developer cognitive loadDetect failures faster and reduce the time to remediation, while also increasing code quality and reliability To understand AI usage, ownership, and success cases

Published
March 25, 2026

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What you'll learn

Key Takeaways

Platforms Enable Safe AI Acceleration

Platform engineering serves as the operating system for AI-driven delivery. It provides the necessary guardrails to safely accelerate development for both human engineers and AI agents.

The Evolving Role of Software Engineers

AI is not replacing developers but shifting their core responsibilities. Engineers will transition from deep focused coding to directing AI agents and managing complex architectural decisions.

Automated Assurance Replaces Manual Code Reviews

As AI dramatically increases the volume of generated code, traditional manual reviews become unsustainable bottlenecks. Organizations must shift toward automated security guardrails and a review-by-exception model.

Humans Retain Accountability for AI Code

Even when AI agents generate the software, human engineers remain fully accountable for what ships to production. Teams must focus on building resilient systems and learning from failures rather than blaming the AI.

Intentional Apprenticeships for Junior Developers

Because AI handles many entry-level coding tasks, organizations need strong apprenticeship models. This ensures junior engineers still learn the critical architectural and design skills required to become technical leaders.