AI Agent Reliability & Evals
On-call reliability, eval suites, observability, and model/cost optimization for production AI agents.
If an AI agent is already in production, someone has to own regressions, hallucinations, retrieval drift, observability, incidents, and provider changes. We embed as a senior AI reliability partner: audit the system, build evals from your real data, wire monitoring, fix the highest-risk failures, and keep quality measurable over time.
What you get
- ✓<2hr response time on production agent incidents for retained engagements
- ✓Reusable eval suite that catches regressions before they reach users
- ✓Quarterly model, retrieval, and inference-cost review with rollback gates
Who it's for
- —SaaS teams with an AI feature live in production and no clear owner for reliability
- —Teams whose chatbot or agent makes things up and needs before/after numbers
- —Founders who need senior AI engineering support without hiring a full-time AI lead
How we work
A real engagement, week by week.
- 1Week 1
Reliability audit
- Read prompts, retrieval stages, tool specs, evals, and production logs
- Build or review a representative eval set from your real data
- Deliver severity-ranked findings across hallucination, latency, cost, and tool failures
- 2Week 2–3
Baseline + observability
- Wire an eval harness using the tooling that fits your stack
- Add observability for traces, costs, latency, retrieval quality, and failure modes
- Create the first before/after quality report and launch thresholds
- 3Month 2+
Fix + monitor
- Ship prompt, retrieval, guardrail, and tool-call fixes through your PR flow
- Review reliability weekly with your engineering or product lead
- Respond to incidents and regressions inside the agreed support window
- 4Quarterly
Business review
- Benchmark current models against newer or cheaper alternatives
- Review quality, usage, latency, and cost trends
- Agree the next reliability and product bets based on measured behavior
Why us
Why us.
We have shipped production agents on Spring + Gemini with pgvector retrieval for Iris/Nous, so the failure modes are familiar.
Our standalone eval offer turns quality into an artifact: eval set, harness, report, fixes, and re-measurement.
You get senior engineering judgment at a fair global rate, with direct access to the person doing the work.
Common questions
Things prospects ask first.
Yes. We can own the reliability loop for one to three agents while your product team keeps roadmap ownership.
Yes. The eval audit starts from $2.5K and gives you an eval set, report, top failure modes, and a practical fix plan.
We are tool-agnostic. LangSmith, Braintrust, Phoenix, Helicone, OpenTelemetry, or a lightweight custom harness can all work depending on the stack.
Fine-tuning is not the default. Most reliability problems are retrieval, prompting, tool design, eval coverage, or product-boundary problems first.
Ready to start this?
20-minute scoping call. We'll tell you straight whether it's a fit.