AI Agent Evaluation & Observability_
>> Agents fail silently across tool steps. We wire tracing (LangSmith, Phoenix, OpenTelemetry, or Agents SDK spans), offline eval suites, online scorers, and CI gates so regressions never ship on vibes.
Calls/month
Production AI volume across live systems
PII detection
Compliance-ready detection accuracy in production AI
Projects
Shipped since 2012 with senior engineers only
Direct answer
If quality cannot fail a merge, you have dashboards, not evals. We wire traces, offline suites, and CI gates so agent regressions surface before customers do. Cost and latency budgets included.
Trusted by leaders
>> System map
Adjacent AI layers we ship with this expertise. Technology-first, not vendor theater.
>> When this system fits
Honest fit gates. We will tell you when another approach is better.
Strong fit
- Teams shipping agents weekly who need merge blockers on quality
- Products that need cost, latency, and quality budgets in one operating model
- Incident response that must reconstruct tool/handoff paths
Weak fit
- One-off demos with no release process
- Vanity dashboards without scorers or datasets
Stack: Evals · Tracing · LangSmith · Phoenix · Braintrust · OpenTelemetry · CI
What we deliver
_> Capabilities on this stack
Offline eval suites
Golden datasets, scorers, and statistical checks before release.
Online evaluation
Sample production traces with LLM-as-judge or code scorers.
Trace depth
Agent, tool, handoff, and guardrail spans that debug multi-step failures.
CI gates
PR checks that block merges when quality or cost regressions appear.
Eval and observability rollout
Define failure modes
Name the quality, cost, and safety failures that matter to the business.
Instrument and baseline
Ship traces and a minimal offline suite against real examples.
Gate releases
Wire CI blockers and convert production failures into new eval cases.
Related projects
_> See how we've applied our expertise
>>Related guides
_> Cite-worthy depth behind this stack
Frameworks and scorecards buyers and answer engines can quote. Each guide links back to delivery proof.
Agent Eval, Cost, and CI Gates
Build offline eval suites, online scorers, cost budgets, and merge-blocking CI gates for production agents.
AI Observability for SaaS Leaders: LLM Quality, Latency, Cost
A practical guide to AI observability in SaaS: track LLM quality, latency, and cost in a boilerplate stack with concrete metrics, tests, and rollout steps.
RAG Quality Guide: Evaluation That Holds Up in Production
A practical guide to RAG evaluation: offline test sets, retrieval and answer metrics, regression gates, and RAG monitoring to reduce hallucinations.
E2E testing for LLM SaaS: deterministic tests, goldens, CI/CD
A practical end to end testing strategy for AI assisted SaaS: deterministic seams, golden datasets, evaluation gates, and CI/CD patterns for LLM features.
FAQ
Depends on the job. Braintrust-class tools excel at eval/CI gates. LangSmith is deepest in LangChain ecosystems. Phoenix/Langfuse fit OpenTelemetry and self-host needs. Hybrid stacks are common.
No. Evals catch regressions at scale. Humans still own high-risk review and dataset curation.
>> Where this goes next
Adjacent expertise and the engagement models we deliver it through.