{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Agents and Engineers","title":"Skills, Context, and Trust: The New Agentic Coding Stack","html":"","width":"100%","height":180,"duration":4636,"description":"Dan and Jonathan Bown open with the talk Jonathan gave at ODSC, \"Practical Agent Ops: From POC to Prod with MLflow 3.0.\" MLflow 3.0 arrived last summer as the first stable release built for generative AI rather than traditional machine learning, and Jonathan's team used it to build an agent for pre-enrollment students. The centerpiece of that work was evaluation-driven development. Instead of jumping straight into a working prototype they aligning the business up front on what quality actually looks like before signing off on a model with inherently non-deterministic output.\nThe initial key to success was an Excel file. In it, the data science team had already assembled 150 ground truth examples, but left them untested and set aside while engineers focused on code. Jonathan's team paused the coding work and ran a simple foundation model against those examples first, landing at what amounted to a coin flip of useful versus hallucinated answers. From there they refined the examples with the business, loaded them into MLflow's evaluation datasets built from live traces, and iterated by versioning prompts and agent configurations.\nTooling came up repeatedly. MLflow's open source repo now ships a skill file that plugs into coding tools like Claude Code, which Jonathan called a game changer for keeping up with an API that changes at roughly a release a month. The Databricks AI Dev Kit, released around March, bundles skills for the Databricks SDK, CLI, data engineering, and analytics work, usable either inside Databricks' Genie Code pane or in outside tools such as Claude Code, AWS Kiro, or Google Antigravity. Jonathan said installing it produced a dramatic jump in output accuracy compared to coding assistants working from stale or incomplete context about Databricks and MLflow APIs.\nDan raised the idea that LLMs and agentic tools are becoming users of software in their own right, alongside humans, and Jonathan tied that to broader changes at WGU: more of the business,...","thumbnail_url":"https://img.transistorcdn.com/TK0Nqa_Yt1Nidvhw7SvORku00Quhyrr-EpS6aCekMzA/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9hMmU5/YmJlYjNlY2E2ZThh/OGYwZmExY2M5MGMz/MDQyNC5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}