Beyond DORA: Metrics, AI and the future of platform engineering

The podcast explores the intersection of AI and platform engineering, particularly addressing how organizations can effectively leverage AI to solve real problems. It emphasizes that successful AI implementation requires a strong engineering foundation, including quality internal platforms, APIs, and data. The discussion highlights the importance of focusing on team metrics over individual productivity and tailoring metrics to specific business goals, balancing quantitative data with qualitative feedback. The panelists share experiences and insights on avoiding the trap of "AI for AI's sake," advocating for a problem-oriented approach and addressing technical debt to fully realize AI's potential. They also touch upon the evolving role of platform engineering in supporting citizen developers and the need for a balanced approach between randomness and determinism in AI applications.

Outlines

Part 1: Problem Definition, Strategy

Part 2: Foundations, Infrastructure

Part 3: Measuring Impact, Metrics

Part 4: Determinism, Agentic Workflows

Part 5: Future Outlook, Organizational Innovation

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Platform Engineering

Part 1: Problem Definition, Strategy

Addressing C-Level AI Mandates: Prioritizing Problem Definition Over Hype

AI's Impact on Developer Experience and Platform Architecture Principles

Solving Real Problems: AI in Modernization Efforts and Avoiding Strategy by FOMO

Part 2: Foundations, Infrastructure

DORA Report Insights: AI Capabilities and the Importance of Internal Platforms

Differentiators in AI: Human Training, APIs, and the Importance of Foundations

Technical Debt as a Barrier: Predictions for AI Solution Scalability

Part 3: Measuring Impact, Metrics

The Golden Age of Newcomers: Overcoming Complacency and Measuring AI Impact

Measuring Impact: Maximizing Value and Optimizing Resource Allocation

Telemetry and Observability: Balancing Data Collection with Principled Investment

Expanding Telemetry Thinking and Avoiding Overwhelming Metrics

Qualitative Measurements and the McNamara Fallacy in AI

Time to Information as a KPI and Optimizing for Humans and AI

Part 4: Determinism, Agentic Workflows

Determinism in AI: Balancing Inference with Predictable Outcomes

Engineering vs. Business Platforms: Workflow Engines and Agentic Workflows

Part 5: Future Outlook, Organizational Innovation

Platform Engineering for Citizen Developers and the Factorization of Knowledge Work

BDD as a Platform Abstraction and the Struggle for Practical Engineering

Platform as Abstraction: Socio-Technical Problems and Organizational Innovation

Beyond DORA: Metrics, AI and the future of platform engineering

Platform Engineering

Part 1: Problem Definition, Strategy

00:00Addressing C-Level AI Mandates: Prioritizing Problem Definition Over Hype

Addressing C-Level AI Mandates: Prioritizing Problem Definition Over Hype

01:31AI's Impact on Developer Experience and Platform Architecture Principles

AI's Impact on Developer Experience and Platform Architecture Principles

06:00Solving Real Problems: AI in Modernization Efforts and Avoiding Strategy by FOMO

Solving Real Problems: AI in Modernization Efforts and Avoiding Strategy by FOMO

Part 2: Foundations, Infrastructure

09:07DORA Report Insights: AI Capabilities and the Importance of Internal Platforms

DORA Report Insights: AI Capabilities and the Importance of Internal Platforms

12:21Differentiators in AI: Human Training, APIs, and the Importance of Foundations

Differentiators in AI: Human Training, APIs, and the Importance of Foundations

15:24Technical Debt as a Barrier: Predictions for AI Solution Scalability

Technical Debt as a Barrier: Predictions for AI Solution Scalability

Part 3: Measuring Impact, Metrics

17:40The Golden Age of Newcomers: Overcoming Complacency and Measuring AI Impact

The Golden Age of Newcomers: Overcoming Complacency and Measuring AI Impact

22:30Measuring Impact: Maximizing Value and Optimizing Resource Allocation

Measuring Impact: Maximizing Value and Optimizing Resource Allocation

26:16Telemetry and Observability: Balancing Data Collection with Principled Investment

Telemetry and Observability: Balancing Data Collection with Principled Investment

28:04Expanding Telemetry Thinking and Avoiding Overwhelming Metrics

Expanding Telemetry Thinking and Avoiding Overwhelming Metrics

32:34Qualitative Measurements and the McNamara Fallacy in AI

Qualitative Measurements and the McNamara Fallacy in AI

35:39Time to Information as a KPI and Optimizing for Humans and AI

Time to Information as a KPI and Optimizing for Humans and AI

Part 4: Determinism, Agentic Workflows

38:09Determinism in AI: Balancing Inference with Predictable Outcomes

Determinism in AI: Balancing Inference with Predictable Outcomes

40:45Engineering vs. Business Platforms: Workflow Engines and Agentic Workflows

Engineering vs. Business Platforms: Workflow Engines and Agentic Workflows

Part 5: Future Outlook, Organizational Innovation

43:57Platform Engineering for Citizen Developers and the Factorization of Knowledge Work

Platform Engineering for Citizen Developers and the Factorization of Knowledge Work

47:07BDD as a Platform Abstraction and the Struggle for Practical Engineering

BDD as a Platform Abstraction and the Struggle for Practical Engineering

51:54Platform as Abstraction: Socio-Technical Problems and Organizational Innovation

Platform as Abstraction: Socio-Technical Problems and Organizational Innovation