Agent Infrastructure Research
Memory, RAG, inference, and runtime systems for autonomous agents
Our agent infrastructure research focuses on building the core primitives autonomous agents need to operate — from persistent memory and retrieval systems to optimized inference pipelines and multi-agent coordination runtimes.
Active Research Areas
Memory Engineering
Developing advanced memory architectures for LLMs to maintain context over extended interactions
- •Long-term memory systems
- •Context compression techniques
- •Semantic memory indexing
- •Memory retrieval optimization
RAG Systems
Building robust Retrieval-Augmented Generation pipelines for enhanced knowledge integration
- •Vector database optimization
- •Hybrid search strategies
- •Document chunking algorithms
- •Real-time knowledge updates
LLM Optimization
Fine-tuning and optimizing large language models for specialized domains
- •Parameter-efficient fine-tuning
- •Model quantization techniques
- •Inference acceleration
- •Multi-modal integration
Agent Systems
Creating autonomous AI agents capable of complex reasoning and tool use
- •Tool-use frameworks
- •Multi-agent coordination
- •Goal-oriented planning
- •Self-improvement mechanisms
Current Projects
Sandstorm
A vendor-agnostic Sandbox Routing Layer that provides one 5-line SDK (sandstorm.run(code, spec)) that dispatches to E2B, Daytona, Modal, Apple Containers, Morph, your own Kubernetes cluster, or rootless edge agents. Features smart arbitrage engine for optimal backend selection, unified telemetry & billing, and pluggable policies for custom isolation rules.
Recent Publications
Coming Soon