Distributed Systems
Coordination fabric for training, agents, and data.
Distributed Systems research at Zansoc covers the coordination problems behind large training jobs, multi-agent platforms, and globally replicated data planes. We build primitives for consensus, streaming, and fault-tolerant orchestration that AI products can rely on.
Training Orchestration
Elastic jobs with checkpoint and resume guarantees.
Event Fabrics
High-throughput streams for sensor and agent traffic.
Consistency Tools
Patterns for stateful agents and shared memory.
Failure Testing
Chaos and game-day tooling as standard practice.
AI workloads are distributed systems
A multi-agent workflow is a distributed transaction with fuzzy side effects. We apply the same rigor — idempotency, timeouts, backpressure — that we apply to databases, then extend it for long-running LLM calls and human approval steps.
Training that survives reality
Preemptible capacity, flaky interconnects, and partial node loss are normal. Our orchestration layer checkpoints aggressively, reshards when topology changes, and reports progress in terms operators understand.
Shared with the Labs
The Distributed Computing Lab uses these primitives as instruments; product teams consume them as managed services. Research and production share the same failure models.