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Sep 26
37m 11s

The evolving role of DevProd teams in th...

DX
About this episode

CEO Abi Noda is joined by DX CTO Laura Tacho to discuss the evolving role of Platform and DevProd teams in the AI era. Together, they unpack how AI is reshaping platform responsibilities, from evaluation and rollout to measurement, tool standardization, and guardrails. They explore why fundamentals like documentation and feedback loops matter more than ever for both developers and AI agents. They also share insights on reducing tool sprawl, hardening systems for higher throughput, and leveraging AI to tackle tech debt, modernize legacy code, and improve workflows across the SDLC.

Where to find Abi Noda:

• LinkedIn: https://www.linkedin.com/in/abinoda  

• Substack: ​​https://substack.com/@abinoda  


Where to find Laura Tacho: 

• LinkedIn: https://www.linkedin.com/in/lauratacho/

• X: https://x.com/rhein_wein

• Website: https://lauratacho.com/

• Laura’s course (Measuring Engineering Performance and AI Impact): https://lauratacho.com/developer-productivity-metrics-course


In this episode, we cover:

(00:00) Intro: Why platform teams need to evolve

(02:34) The challenge of defining platform teams and how AI is changing expectations

(04:44) Why evaluating and rolling out AI tools is becoming a core platform responsibility

(07:14) Why platform teams need solid measurement frameworks to evaluate AI tools

(08:56) Why platform leaders should champion education and advocacy on measurement

(11:20) How AI code stresses pipelines and why platform teams must harden systems

(12:24) Why platform teams must go beyond training to standardize tools and create workflows

(14:31) How platform teams control tool sprawl

(16:22) Why platform teams need strong guardrails and safety checks

(18:41) The importance of standardizing tools and knowledge

(19:44) The opportunity for platform teams to apply AI at scale across the organization

(23:40) Quick recap of the key points so far

(24:33) How AI helps modernize legacy code and handle migrations

(25:45) Why focusing on fundamentals benefits both developers and AI agents

(27:42) Identifying SDLC bottlenecks beyond AI code generation

(30:08) Techniques for optimizing legacy code bases 

(32:47) How AI helps tackle tech debt and large-scale code migrations

(35:40) Tools across the SDLC


Referenced:

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