Alan Shimel discusses with Aman Sardana and Vijay Kumar Soni the evolving role of AI in DevOps at swampUP 2025, focusing on how metrics like speed, trust, and transparency are reshaping modern software delivery pipelines.

The New Metrics of DevOps: Speed, Trust and Transparency

Author: Alan Shimel

Overview

At swampUP 2025, Alan Shimel interviews Aman Sardana and Vijay Kumar Soni about the changing landscape of DevOps in response to the growing influence of artificial intelligence (AI) across the software development lifecycle (SDLC).

Key Topics

  • Intersection of DevOps and AI:
    • AI is increasingly woven into coding, testing, and deployment workflows.
    • The integration accelerates release velocity but also adds complexity to DevOps.
  • Evolving CI/CD Models:
    • Traditional pipelines now deal with AI-generated code and model deployment.
    • Continuous governance is necessary for AI systems.
  • Need for New Metrics:
    • Speed is not the sole benchmark. Trust and transparency become critical.
    • Automation must be paired with observability, policy enforcement, and risk management.
  • Challenges with AI Systems:
    • Explainability and traceability are essential as AI transitions from deterministic to probabilistic systems.
    • Requires stronger collaboration among platform engineers, data scientists, and security professionals.
  • Responsible Acceleration:
    • Future DevOps aims to maintain control and transparency as AI-powered automation transforms code, infrastructure, and models from design to production.
    • Success will be measured as much by secure and intelligent delivery as raw speed.

Strategic Takeaways

  • Integrate AI into DevOps pipelines with careful attention to governance and risk.
  • Build feedback loops that account for the uncertainty and complexity of AI-driven workloads.
  • Foster multidisciplinary teams combining engineering, data, and security perspectives.
  • Update DevOps success metrics to reflect transparency, trust, and responsible acceleration as code and models move to production.

Further Reading

This post appeared first on “DevOps Blog”. Read the entire article here