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As agents take over more of the actual coding, the nature of technical work is shifting from producing software to judging it. Engineers increasingly spend their time specifying what should be built and then checking whether an agent's output actually solved the problem, rather than writing every line themselves. That changes what skills matter for a career in data and software: problem-solving and evaluation start to outweigh knowing today's specific tools or syntax. It also raises a harder question for teams — if agents can generate work this fast, how do you know which of it is actually worth shipping?
Ledion Bitincka is Co-Founder and CTO of Cribl, where he leads the engineering organization with a first-principles approach to product delivery. Before Cribl, he was an Advanced Development Architect at Splunk, where he worked on Search-Time Schema, Hunk, and SmartStore, and before that founded Triangulus Communications. Nikhil Mungel is Head of AI R&D at Cribl, based in San Francisco, with over 15 years building distributed systems and AI teams at companies including Substack, Splunk, and ThoughtWorks — he now leads teams building LLM-powered systems for IT and security data.
In the episode, Richie, Ledion, and Nikhil explore AI agent disasters and cost shocks, software telemetry fundamentals, using agents to analyze telemetry, AI-powered software factories, the shift from knowledge work to judgment work, skills for the agentic era, avoiding runaway AI spend, and measuring product value through growth metrics, and much more.
Links Mentioned in the Show:
• Jocko Willink's book, Leadership Strategy and Tactics: A Field Manual
• Cribl
• NVIDIA
• Connect with Ledion: LinkedIn
• Connect with Nikhil: LinkedIn
• AI-Native Course: Intro to AI for Work
• Related Episode: AI Agents at Work: What Actually Breaks (and How to Fix It)
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