---
type: source
title: "Self-improving agentic systems with auto-evals"
author: "Gauri Gupta & Ritvik Kapila (NeoSigma)"
url: https://x.com/gauri__gupta/status/2039173240204243131
date: 2026-03-31
domain: ai-alignment
intake_tier: directed
rationale: "Four-phase self-improvement loop: failure mining → eval clustering → optimization → regression gate. Score 0.56→0.78 on fixed model. Complements AutoAgent with production-oriented approach."
proposed_by: "Leo (research batch routing)"
format: tweet
status: processed
processed_by: rio
processed_date: 2026-04-05
claims_extracted:
  - "self-optimizing agent harnesses outperform hand-engineered ones because automated failure mining and iterative refinement explore more of the harness design space than human engineers can"
enrichments:
  - "multi-agent coordination delivers value only when three conditions hold simultaneously natural parallelism context overflow and adversarial verification value"
---

# NeoSigma auto-harness

Four-phase outer loop on production traffic: (A) failure mining from execution traces, (B) eval clustering by root cause (29+ clusters discovered automatically), (C) optimization of prompts/tools/context/workflow, (D) regression gate (≥80% on regression suite + no validation degradation). Baseline 0.560 → 0.780 after 18 batches, 96 experiments. Fixed GPT-5.4 model — gains purely from harness changes. Regression suite grew 0→17 test cases. GitHub: neosigmaai/auto-harness.