AI Agents Self-Correct Causal Analysis Errors via Verifier and Cross-Review
hugobowne · x · 2026-08-03
During a recent Agentic Data Science cohort, Thomas Wiecki demonstrated a compelling case study: an agent initially found the correct result in a causal analysis but then reasoned itself out of it with a convincing but false explanation.
To solve this hallucination problem, he introduced a fresh verifier agent. The verifier ran placebo tests that exposed the mistake, distilled the failure into a reusable skill, and successfully recovered the correct result upon rerunning the analysis.
He also showcased a multi-agent workflow where Codex and Claude Code were given the same analysis independently and then asked to examine each other's work, highlighting a practical approach to cross-verification.
More from coding & agent
- Curated List of 200+ Big Tech AI Engineering Practices and Guides — Al_Grigor · 2026-08-03
- Steve Yegge on the Evolution of AI Coding Agent Architectures — Steve_Yegge · 2026-08-03
- PullMD: Open-Source Tool to Convert Web URLs & Files to Clean Markdown — tom_doerr · 2026-08-03
- 10,000-Hour Programmers Embrace AI: Never Writing Manual Code Again — georgemillo · 2026-08-03
- Open-Source Legal AI: 139 Agent Skills for GDPR and NDA Workflows — tom_doerr · 2026-08-03
- Prompt-Architecture as Literature: Writing Executable Text for LLMs — lnsip9reg · 2026-08-03