Calibrating Autonomy: Why the Best AI Agents Know When to Step Back | CDO Vision Los Angeles 2026

Chetan Thapar, who leads product for Posit's Snowflake integration, shares his take on agent autonomy and what Posit is building next. What's covered: - Why coding assistants, analytical agents, and AI-powered support triage are "table stakes," and where the real value building happens next - Posit's three areas of focus for 2026: a unified AI agent spanning the full data-to-decision pipeline, dynamic visualization powered by semantic models (no more pre-canned dashboards), and deterministic skills that pair LLM orchestration with statistically validated machine learning methods - "Calibrating autonomy": why data science workflows need agents to run independently on verifiable tasks, but stay human-in-the-loop for exploratory analysis, where the real insights often come from noticing something strange in the data - The difference between a plausible AI answer and an actually correct one - Why the next shift in AI is moving from systems that answer questions well to systems that help people ask better ones (with a nod to how Claude Code prompts the prompter) Learn more about Posit for Snowflake: https://posit.co/solutions/snowflake This interview is from CDO Vision Los Angeles March, 2026 (AIM Media House's invite-only summit for Chief Data Officers and enterprise AI leaders)