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A running timeline of what we are working on, publishing, and exploring.
This month we are deep in agent architecture research - testing frameworks, memory systems, and reliability patterns.
Finished comparative analysis of 5 memory architectures. Vector stores with metadata filtering showed 3.2x improvement in task continuity.
New field note on structured output schemas and their impact on tool calling success rates. 80% failure reduction confirmed.
New experiment tracking prompt caching effectiveness across 1000+ API calls. Preliminary data shows 38% cost reduction.
Released our internal tool for tracking prompt iterations with A/B testing capabilities. May open-source later.
Completed first phase of agent framework comparison. LangGraph showing better control flow, CrewAI easier setup.
Set monthly research agenda: multi-agent orchestration, voice interfaces, and local LLM benchmarking.
What is the optimal memory retrieval strategy for long-running agents?
How do we gracefully handle cascading tool failures?
Can we achieve GPT-4 quality on narrow tasks with quantized local models?