Tzar.Tech
Agents that survive production.
We ship agentic AI systems your ops team actually trusts — with evals, traces, guardrails, and rollback plans in the box.
Operations overview
- Promote ingest-v124m
- Raise rate limit 5k/min12m
- Retire legacy webhook1h
Four cues. One console.
Discover, build, deploy, steward — each cue lights a live agent console drawn in markup.
Typed integrations
A full stack for agentic systems.
Five overlapping capabilities. Pick one or all; we fit your stack.
AI Agents & Skills
Multi-step tools with evals, traces, and guardrails.
- Typed contracts
- Eval gates
- Rollback plan
RAG Pipelines
Hybrid retrieval with citations and drift detection.
- Hybrid index
- Source-linked answers
- Drift detection
Durable Execution
Sagas + retries that survive restarts.
- Stateful workflows
- Retry budgets
- Replay debug
A2A Orchestration
Typed message-passing between specialized agents.
- Typed envelopes
- Auth between agents
- Mesh observability
Schema-Driven Dev
Contract → codegen pipelines that keep clients in sync.
- One source of truth
- Codegen for 4 langs
- Versioned contracts
Outcomes, not demos.
They shipped a system that survived our peak weeks. The evals caught two regressions that would have made it to production otherwise.
What we actually ship.
Every system we build follows this shape. Client at the edge, tools in a sandbox, traces everywhere, evaluators gating output. No black boxes, no "it works on my machine."
Watch an agent do the job.
Three real production scenarios, replayed at observed latency. Every box is a span; every span has tokens, cost, and an eval gate. This is what shows up in your traces, not a marketing animation.
Pretrade review requires: applicable-rules retrieval, market-data lookup, position-check, and a deterministic evaluator gate.
One invoice for the whole run.
Every tier is the same delivery system. What changes is the cast, the duration, and how close we stay after launch.
Discovery sprint
Fixed · 2 weeksArchitecture, working spike, build plan. You leave with a repo.
You get- Architecture diagram
- Risk register
- Working prototype
- Build plan + estimate
For teams sizing up an agentic system.- Most common
Build pod
Fixed · 8–14 weeksA pod of 2–4 senior engineers ships in your repo, your stack, against your evals.
You get- Production system
- Eval gates
- Trace dashboards
- Rollback plan
- Runbook
For teams ready to put agents in production. Stewardship
Monthly · rollingWe stay close as the system runs. Tunings, eval expansions, scope additions.
You get- Oncall rotation overlap
- Eval suite maintenance
- Cost + perf reviews
- Quarterly roadmap
For teams owning the system long-term.
Built for production.
- Deterministic + LLM-judge evals run pre-deploy
- Tool-call coverage + schema validation enforced
- Latency + cost budgets gated per release
- Same pod from spike to production
- 12+ years average; ML, distributed systems, oncall
- You meet the engineers in week one, not week ten
- Your repo, your stack, your monitoring
- No vendor lock-in, no proprietary runtime
- Knowledge transfer in week of go-live
Asked at every scoping call.
Fixed-price, scoped to two weeks. We can share a rate sheet on request — the goal is that you leave with something concrete (architecture + spike) regardless of whether you continue with us.
Eight to fourteen weeks from kick-off to production rollout. Discovery is included if you do it with us; otherwise we work from your existing spec.
You do. The repo lives in your org from day one. We commit there. We do not run anything proprietary that you cannot replicate.
Every release runs through eval gates: deterministic checks (schema, latency, cost), tool-call coverage, and LLM-judge spot checks. The deploy stops if any of them regress.
Budgets are per-call and per-day. We cap context with retrieval-truncation, route to cheaper models where the eval allows, and fall back to deterministic paths under cost pressure.
Every path has a fallback. Deterministic logic catches the common cases; the agent path layers on top. Rollback is a one-line route swap behind a feature flag.
Two-week shadowing during ramp, runbook + eval suite delivered in your repo, oncall overlap for the first incident. After that you own it.
Faithfulness (does the answer follow from cited docs?), grounding (are citations real?), and recall (did we find the right docs?). Every release runs the suite.
Yes. Durable execution captures inputs at every step. Replay against a known-bad input reproduces the failure exactly, including non-deterministic LLM calls when seed-able.
Each agent has a typed identity + mTLS by default. We use scoped tokens with short lifetimes; calls between agents carry trace IDs so the auditor can reconstruct the flow.
Additive-only by default, with a `version` field on every envelope and a deprecation policy that's checked in CI. Breaking changes go through a parallel-version window.
Ship the first system.
Fixed-price discovery in 2 weeks. You leave with an architecture, a working spike, and a build plan.