Investigating fundamental questions across agentic systems, modular runtimes, and resource-efficient local intelligence. All projects adhere to our principles of reproducible experimentation and transparent reporting.
Investigating Agentless and Hybrid Intelligence Architectures
Exploring the boundaries between deterministic, agent-based and hybrid approaches to intelligent problem solving. Can we achieve reliable and efficient task execution through deterministic workflows, structured reasoning and selective autonomy?
"Which tasks actually benefit from autonomous agents?"
Researching Modular Execution Architectures for Intelligent Systems
Investigating the architecture and engineering principles behind lightweight, modular intelligent runtimes. Can we develop a modular AI runtime that provides essential orchestration without depending on heavyweight agent frameworks?
"Can we develop a modular AI runtime with essential orchestration capabilities?"
Exploring Small Models, Local Inference and Context Efficiency
Investigating how small language models, local inference, retrieval and intelligent context selection can be combined to deliver efficient AI systems on resource-constrained hardware.
"What performance is achievable from quantised small models on consumer hardware?"