In the news
GEPA-Curator: Curated Learning for Self Improving Agents
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- GEPA-Curator filters which agent failures teach prompt optimizers, using task curation and successful trajectory retrieval to improve learning signals.
- Why it matters
- Engineers building multi-turn LLM agents need this when prompt optimization learns from noisy or unreliable failures across complex environments.
- Watch out
- Tested on specific benchmarks with Gemini and Claude; unclear how curation strategy generalizes to other agent types or domains.
- agent
- agentic
- llm
- language model
- prompt
The patterns behind this
Each one covers how the technique works, when it earns its cost, and where it breaks.
The Agent Architect
One pattern, one tradeoff, one production failure story. A short weekly briefing for people building agentic systems.
Weekly email, one-click unsubscribe. We only use your address to send the briefing.