Technical depth, not marketing. Architecture papers, benchmarks, engineering notes, experiments, and open-source releases — across agent systems, memory, autonomous decisions, and industrial AI.
Designing a runtime that handles planning, tool execution, permissions, and observability at scale — with formal guarantees on execution correctness.
How we built a memory layer that maintains context across millions of agent sessions while preserving privacy, enabling recall, and supporting governance.
Empirical evaluation of perception-to-action latency, decision accuracy under uncertainty, and closed-loop stability across 50,000+ simulated miles.
Constructing queryable knowledge graphs from unstructured technical documentation, enabling evidence-backed troubleshooting for field technicians.
A formal model for tool execution safety in agent runtimes, with static analysis, runtime monitoring, and rollback for irreversible operations.
Domain randomization and curriculum learning strategies for transferring navigation policies from simulation to real-world agricultural environments.
Code, data, and experiment configs published alongside papers.
Claims backed by benchmarks, ablation studies, and real-world deployments.
Core infrastructure open-sourced; research shared with community.
New research, product releases, engineering notes, and open-source work. Low volume, no marketing noise.
