LLM agents that write Python to analyze execution traces at scale
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We combined Stanford's ACE (agents learning from execution feedback) with the Reflective Language Model pattern. Instead of reading traces in a single pass, an LLM writes and runs Python in a sandbox to programmatically explore them - finding cross-trace patterns that single-pass analysis misses. The framework achieved 2x consistency improvement on τ2-bench.
HN · launched Mar 7, 2026 · 5 upvotes (top 89%) · 0 views
Niche: ai agent observability platforms · 220 launches, 5 in the last 7 days ▼ -64% · Explore this niche →
Category and tags
- Horizontal
- Observability & eval
- Developer
- AI-native
- Commercial product
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More like this: all 220 launches in ai agent observability platforms →
Could you build this?
Yes This is an agentic workflow that passes execution traces to an LLM, uses code execution tools (e.g. E2B or Docker sandbox), and loops over results.
Discussion
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Competitors
Other products that read as similar to this one : 2617 launches clear the similarity bar, closest 8 shown.
Attention rank: #2403 of 2618 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 129 days after the earliest competitor.
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