We Scored Oracle's Database Skill Live: 95% Isn't Enough
Learn how Oracle built an AI skill to enforce database best practices, improving SQL generation from 95% to 100% success rate using automated reviews and iterative refinement.
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AI Native Dev
Digests from AI Native Dev
Stephane Jourdan, Simon Rohrer & Pini Reznik - From Pipelines to Prompts: Surviving the Shift to AI
The shift to AI-native development is more dramatic than Cloud/DevOps, requiring new guardrails. Key focus areas include deterministic linting, advanced observability stacks, and harness engineering to manage agent-driven complexity.
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Inside the Dark Factory: AI That Ships Code Solo
Learn how Tessl's Dark Factory ships 70% of its code autonomously using AI agents, sophisticated verifiers, and a robust orchestration layer.
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Harness Engineering: Building an AI Software Factory
Harness engineering is a discipline focused on building automated loops of checks and maintenance agents to delegate more work to AI. This process involves improving three dimensions: autonomy, automation, and quality.
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Simon Obstbaum & Rob Willoughby - Why evals are hard and how we're solving it - AI Native DevCon Jun
Moving beyond simple output checks, effective AI agent evaluation requires measuring trajectory and skill activation. Learn how structured context improves code quality metrics like PR throughput and adherence to unique business IP.
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Tammuz Dubnov - When Our PM Started Writing Code: What Merge Rate Taught Us About AI Adoption - AI N
AI adoption shifts bottlenecks from coding speed to coordination and alignment. Success is measured by the merge rate of PRs opened by non-technical users, proving that empowering decision-makers is key.
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Dave Farley - Vibe Coding - Is this really the best we can do? - AI Native DevCon June 2026
AI assistance is rather like the compiler. We're not going to care for very much longer at all if we do now about the code that it generates cuz we'll verify that we got the results.
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Oleg Šelajev - You're absolutely right, it was your home directory! - AI Native DevCon June 2026
Docker introduces sandboxing using MicroVMs to safely run autonomous AI agents locally. This hardware-level isolation prevents rogue agents from accessing sensitive host data, balancing speed and security.
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Why AI Agents Need a Data Harness, Not Just a Lakehouse
AI agents require a 'data harness' optimized for conversational pace. This requires combining open standards (Iceberg, Arrow) with a semantic layer to ensure data is accessible, understandable, and performant.
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Brian Douglas - The beginners guide to training AI on your own code - AI Native DevCon June 2026
Discover how to build self-healing AI agents by capturing every interaction trace. Learn about Merkle DAGs, Flink SQL anomaly detection, and using proprietary data for specialized model fine-tuning.
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BONUS EPISODE: 76 Malicious AI Skills Were Hiding in Plain Sight
This session unpacks the security risks of AI coding agents, detailing how 76 malicious skills were found. Learn about prompt injection, secure registries (Tessl), and Snyk’s approach to governing agent behavior at scale.
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Robert Overweg - One Brain, No Filtering - AI Native DevCon June 2026
Discover how to build a 'One Brain' using AI agents and structured vaults. Learn about OpenClaw, semantic search, and transforming fragmented organizational knowledge into actionable insights.
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Tessl Skills Clinic - Nnenna Ndukwe from Qodo
Learn how to optimize AI agent skills using automated reviews. We analyze Qodo's PR Resolver skill, boosting its score from 78% to 89% by refining metadata and structure.
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Simon Martinelli - Lessons from Spec-driven Development - AI Native DevCon June 2026
Discover spec-driven development using SysML use cases and entity models as the single source of truth for enterprise applications. Learn how to modernize legacy systems by shifting complexity left into requirements engineering.
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Why This Company Won't Let AI Agents Touch Bash
The talk outlines how building robust AI agents requires moving beyond simple LLM calls. Key guardrails include whitelisting tools, using Knowledge Graphs over RAG, and enforcing structured citations.
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Jack Wotherspoon - Humans vs. Slop: Rewriting the Rules of Open-Source - AI Native DevCon
The rise of AI agents is flooding open-source projects with 'slop'—low-quality, unvetted code. Maintainers must implement new guardrails like requiring issues first and using automated systems to manage contributions.
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Edouard Maleix - How AI-First Dev Teams Build Collective Intelligence — One Attributed Mistake at
Learn how to build collective intelligence by turning agent mistakes into reusable, attributable knowledge. The process involves Identity $\rightarrow$ Diary $\rightarrow$ Knowledge Pack.
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May Walter - From Blind Spots to Merged PRs: Runtime Intelligence for Continuous Agentic Performance
May discusses adding runtime intelligence to coding agents, enabling continuous performance optimization by analyzing production context. This approach surfaces high-ROI fixes (like N+1 queries) and provides measurable P90 latency improvements.
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Daniel Jones & Tomasz Maj - More software, faster - Odevo's AI Native transformation - AI DevCon '26
Odevo shares its AI-native transformation journey, detailing how adopting agentic coding requires fundamental process changes—not just licenses—to achieve massive gains in software delivery speed.
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Peter Wilson & Davide Eynard - cq - Stack Overflow for Agents - AI Native DevCon June 2026
CQ (Stack Overflow for agents) standardizes knowledge sharing across AI agents. It allows lessons learned from solving novel problems to be captured as Knowledge Units (KUs), preventing repeated mistakes in automated workflows.
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