m3taversal
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- What: 4 new claims (LLM KB compilation vs RAG, filesystem retrieval over embeddings, self-optimizing harnesses, harness > model selection), 4 enrichments (one-agent-one-chat, agentic taylorism, macro-productivity null result, multi-agent coordination), MetaDAO entity financial update ($33M+ total raised), 6 source archives - Why: Leo-routed research batch — Karpathy LLM Wiki (47K likes), Mintlify ChromaFS (460x faster), AutoAgent (#1 SpreadsheetBench), NeoSigma auto-harness (0.56→0.78), Stanford Meta-Harness (6x gap), Hyunjin Kim mapping problem - Connections: all 4 new claims connect to existing multi-agent coordination evidence; Karpathy validates Teleo Codex architecture pattern; idea file enriches agentic taylorism Pentagon-Agent: Rio <244BA05F-3AA3-4079-8C59-6D68A77C76FE>
23 lines
1.4 KiB
Markdown
23 lines
1.4 KiB
Markdown
---
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type: source
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title: "AutoAgent: autonomous harness engineering"
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author: "Kevin Gu (@kevingu, thirdlayer.inc)"
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url: https://x.com/kevingu/status/2039874388095651937
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date: 2026-04-02
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domain: ai-alignment
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intake_tier: directed
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rationale: "Self-optimizing agent harness that beat all human-engineered entries on two benchmarks. Model empathy finding (same-family meta/task pairs outperform cross-model). Shifts human role from engineer to director."
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proposed_by: "Leo (research batch routing)"
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format: tweet
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status: processed
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processed_by: rio
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processed_date: 2026-04-05
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claims_extracted:
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- "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"
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enrichments:
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- "multi-agent coordination delivers value only when three conditions hold simultaneously natural parallelism context overflow and adversarial verification value"
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---
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# AutoAgent
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Open-source library for autonomous harness engineering. 24-hour optimization run: #1 SpreadsheetBench (96.5%), #1 GPT-5 on TerminalBench (55.1%). Loop: modify harness → run benchmark → check score → keep/discard. Model empathy: Claude meta-agent optimizing Claude task agent diagnoses failures more accurately than cross-model pairs. Human writes program.md (directive), not agent.py (implementation). GitHub: kevinrgu/autoagent.
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