Technical Research
We publish findings on AI safety, agent planning latency, structured data output parsing, and deterministic tool pipelines.
Determinism
We design tools that guarantee output conformity through JSON Schema validation before tools are called by the system.
Efficiency
Our research focuses on minimizing token overhead and API roundtrips during dense multi-agent planning cycles.
Publications & Technical Reports
Safe Multi-Agent Coordination via Sentinel Guardrails
This technical report proposes the Sentinel protocol, a lightweight schema validation and real-time execution sandboxing framework designed to eliminate malicious or unstable tool-use actions in autonomous agent networks.
Optimizing Context Window Latency in Agentic Memory Retrieval
We examine memory structures for agent planning and propose an indexed vector-cache system that reduces memory retrieval latency by up to 43% under dense multi-turn conversation logs.
Self-Correction Loops in Small Language Model Tool-Execution
An analysis of validation loops inside 8B-parameter open-source models, demonstrating that dual-phase feedback loops can increase tool-calling accuracy without fine-tuning overhead.