In the 7th episode of the AI x DevOps podcast, host Rohit Raveendran sits down with Dr. Rahul Vilas Ghodke, SVP & Business Unit Leader – Global Technology Operations (APAC) at CGI, to explore what it really takes to move from AI assistants to AI coworkers in enterprise operations.

Key topics covered:

• Agentic Operations at Scale: Why successful AI-driven operations require decades of automation maturity before agents can be trusted with critical workflows.

• Classic AI vs Generative AI: Understanding when machine learning and deterministic systems deliver better outcomes than LLMs.

• Human-on-the-Loop Operations: How enterprises are balancing automation and oversight for critical infrastructure and business applications.

• Making Tribal Knowledge Discoverable: The role of knowledge governance, prompt catalogs, and curated repositories in improving agent effectiveness.

• Sovereign AI and Hybrid Infrastructure: The challenges of building interoperable AI infrastructure while avoiding vendor lock-in across clouds and hardware vendors.

In the 6th episode of the AI in DevOps podcast, host Rohit Raveendran sits down with Nathen Harvey, the lead at DORA at Google Cloud, to dissect the groundbreaking findings of the 2025 DORA Report.

Key topics covered:

• AI as an Amplifier: Learn why AI is categorized as an "amplifier" rather than a "magic wand," requiring solid existing practices to truly yield results.

• The Platform Engineering Boom: A look into why 90% of survey respondents have now adopted platform engineering.

• The J-Curve of Productivity: How to navigate the initial performance dip during a transformation to reach higher stability and efficiency.

• AI-Centric UX: Discussing whether platforms should be redesigned to serve AI agents as primary users.

• Measuring Success: Moving beyond static dashboards toward team reflection and experimentation to improve software delivery

In this episode of AI x DevOps, Rohit sits down with Görkem Ercan, CTO at Jozu, a company building a DevOps platform for AI agents and models. Görkem, a veteran with over two decades of software experience (including contributions to the Eclipse Foundation), explains why MLOps is fundamentally different from traditional, deterministic DevOps—leading to extreme pipeline fragmentation.

Here are some of our favourite takeaways:

• Standardization is Key: Why OCI is the recognized standard for packaging AI/ML artifacts, and how the Model Packs project (with ByteDance, Red Hat, and Docker) is defining the artifact structure.

• Open Source Headaches: The critical challenge maintainers face when receiving large amounts of untested, verbose, AI-generated code.

• LLM Economics: Discover why running small, fine-tuned LLMs in-house can be cheaper and provide more predictable, consistent results than generic large providers.

• KitOps Solution: How KitOps creates an abstraction that allows data scientists to focus on training while leveraging existing DevOps platforms for deployment.

Tune in now to understand the standardization movement reshaping the future of AI development!