Agent swarms
COORDINATION AT SCALE
Thousands of agents running in parallel with typed message-passing, supervisor trees, and bounded blast radius. Built on durable execution so failures recover, not cascade.
A senior engineering team building agent farms — thousands of agents running in parallel, with the evals, traces, and rollbacks that keep them alive past day 30.
Most agentic systems shipped today are demos that work on the happy path and break on day 30. The interesting work is everything around the model: evals, traces, contracts, rollback plans, observability, on-call handoffs — and the orchestration layer that lets a swarm of agents stay correct as they scale.
We are not an ML lab. We do not train new models. We take the best models available and engineer the rest of the system around them — coordination protocols, durable execution, agent farms with autoscaling — so they survive production and the team that owns the code after we leave understands every line.
Every engagement starts with a fixed-price discovery sprint. You leave with an architecture and a working spike regardless of whether you continue with us. We think this is the most honest way to scope this kind of work.
The model is the easy part. The swarm around it is the engineering.— Tzar.Tech, Engineering principle
Thousands of agents running in parallel with typed message-passing, supervisor trees, and bounded blast radius. Built on durable execution so failures recover, not cascade.
Autoscaling pools, region-aware routing, p99 latency budgets, per-tenant quotas. The same plumbing that runs 12 agents in dev runs 12,000 in prod without re-architecture.
Offline regression suites + online drift detection. Every prompt change ships with eval results; every prod release with rollback boundaries.
Per-agent traces with token accounting, cost attribution by tenant/feature, paging on real anomalies — not on every transient model hiccup.
Sofia · Est. 2018
Fixed-price discovery in 2 weeks. You leave with an architecture, a working spike, and a build plan.