// Production scaffolding — evals, tracing, batch, review
const runtime = new AgentRuntime({
model: "claude-sonnet-4-6",
context: { strategy: "rolling", maxTokens: 180_000 },
tracing: { exporter: "otlp", endpoint: OTEL_URL },
costs: { hardCapUsdPerRun: 0.75, alertAt: 0.5 },
retries: { budget: 3, backoff: "jittered-exponential" },
batch: { api: "messages", window: "24h" }, // ~50% cost
review: { routeIfConfidence: "< 0.85" }, // human queue
});
await runtime.deploy({ env: "prod", vpc: "isolated" });
// ✓ evals: 47/47 ✓ SOC2 logging on ✓ p95 < 8sWhat it is
The demo ran in a Jupyter notebook. Production runs under load, with concurrent users, flaky networks, long-context handoffs, and a finance team watching the cloud bill. The gap between demo and production is where most AI projects stall — not because the model is wrong, but because nobody built the scaffolding around it.
We ship that scaffolding: eval harnesses tied to real business outcomes and wired into CI, trace logging and cost attribution so you can see what agents are actually doing, context-window management across long docs and multi-agent handoffs, and SOC 2-ready deployments on AWS (Bedrock), GCP (Vertex), or Vercel with VPC isolation, audit logging, and auth. For non-blocking workloads — nightly reports, weekly audits, bulk extraction — we route through the Message Batches API for ~50% cost savings. For anything acting on real-world data, we add confidence-calibrated human review: field-level confidence scores, stratified sampling, and accuracy segmented by document type so you see where the model actually works and where it doesn't. Multi-source synthesis preserves provenance — every claim keeps its source, and conflicts get annotated instead of silently resolved. Lifecycle hooks, retry budgets, and circuit breakers round out the reliability layer.
What you get
- 01 Eval harness in CI Automated test suite with cases drawn from real production examples, wired to your pipeline so regressions block merge.
- 02 Tracing & cost dashboards Structured span logging for every agent run plus per-task cost attribution and budget alerting.
- 03 Context management Rolling windows, summarization, and handoff patterns for long docs, multi-turn, and multi-agent workflows.
- 04 SOC 2-ready deployment VPC-isolated runtime on AWS, GCP, or Vercel with audit logging, secrets management, and SSO/API-key auth.
- 05 Reliability patterns Lifecycle hooks, retry budgets, circuit breakers, dead-letter handling, and PII redaction baked into the runtime.
- 06 Batch & human review Message Batches API for non-blocking workloads and confidence-calibrated human review queues for anything with real-world blast radius.
- 07 On-call handoff Architecture diagram, runbook, incident playbook, and on-call guide — the docs your team actually needs at 2am.
How we engage
A process designed for production.
Readiness review
We audit the existing system for eval coverage, observability gaps, cost controls, and compliance posture before proposing changes.
Harness & tracing
Eval harness, trace exporters, and cost dashboards built against your real production data — not synthetic benchmarks.
Deployment hardening
VPC isolation, auth, secrets, queue topology, and reliability patterns staged in a production-equivalent environment and load-tested.
Rollout & on-call
Phased production rollout with traffic shadowing and a 30-day on-call support window on your shared Slack.
Tech stack
Ready to build something that actually works?
We start every engagement with a two-week discovery sprint. No retainer required. You walk away with a spec whether you build with us or not.
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