The Missing Piece of Agent Self-Improvement
Tiger Cloud features Hypercore, a hybrid storage engine integrated into TimescaleDB that optimizes time-series workloads by combining row and columnar storage. Data is initially ingested into row storage for speed and automatically transitioned to compressed columnar storage as it ages. This unified approach eliminates the need for separate read/write systems and complex maintenance pipelines. The system further accelerates analytical queries by accessing batch metadata directly for common summary functions. AI agents often lack a continuous self-improvement loop, especially regarding production failures. The Hermes agent addresses this by saving successful strategies as reusable skills and using GEPA for offline prompt optimization. Opik complements this by providing observability and a diagnostic agent named Ollie that turns production traces into actionable code fixes and regression tests. Together, these tools create a closed loop where agents learn from both successes and failures without requiring manual retraining.
閱讀原文 ↗Postgres for time-series workloads at any scale
Tiger Cloud features Hypercore, a hybrid storage engine integrated into TimescaleDB that optimizes time-series workloads by combining row and columnar storage. Data is initially ingested into row storage for speed and automatically transitioned to compressed columnar storage as it ages. This unified approach eliminates the need for separate read/write systems and complex maintenance pipelines. The system further accelerates analytical queries by accessing batch metadata directly for common summary functions.
- Tiger Cloud utilizes the Hypercore hybrid storage engine to unify write and read workloads.
- Data is initially stored in row format for performance and later converted to columnar format for efficiency.
- Hypercore can compress aging data by up to 95% using its columnar storage engine.
- Summary queries like COUNT and MAX are optimized to read directly from batch metadata.
- The platform removes the need for maintaining complex pipelines between separate read and write systems.
- New users are eligible for $1,000 in free credits on Tiger Cloud.
The missing piece of agent self-improvement
AI agents often lack a continuous self-improvement loop, especially regarding production failures. The Hermes agent addresses this by saving successful strategies as reusable skills and using GEPA for offline prompt optimization. Opik complements this by providing observability and a diagnostic agent named Ollie that turns production traces into actionable code fixes and regression tests. Together, these tools create a closed loop where agents learn from both successes and failures without requiring manual retraining.
- Hermes captures successful strategies in SKILL.md files to enable runtime learning and reuse.
- GEPA (Genetic-Pareto Prompt Evolution) performs offline evolutionary searches to improve prompts and tool descriptions without model retraining.
- Opik is an open-source observability platform that records agent execution traces across over fifty agent frameworks.
- The Ollie coding agent analyzes Opik traces to identify failure points and propose Git-style diff fixes for developer approval.
- Opik's Agent Sandbox allows developers to verify fixes by rerunning agents against the exact scenarios that caused failures.
- Production failures can be converted into regression tests using LLM-as-a-judge evaluations within the Opik workflow.