AI Evals + Observability
Test, trace, and keep agents honest in production.
The production-ops layer for AI systems. Eval harnesses with grounded ground truth, prompt-regression suites across model versions, trace + cost + latency tracking, guardrails on inputs and outputs, red-team batteries, and routing across providers so a single outage doesn't take you down.
What you get
4 pillarsEval harnesses
Bounded test suites with curated ground truth — quality measured on a number, not on vibes. CI-gated thresholds before any model swap.
Trace + cost + latency
Langfuse / LangSmith / Helicone wiring — every prompt, tool call, and response captured with token cost and p50/p95 latency. Drift alerts.
Guardrails + red-team
Input filters, output filters, jailbreak detection, PII redaction. Adversarial prompt batteries run pre-deploy and on a schedule.
Model routing + fallback
Multi-provider routing with health-aware fallback (Claude → GPT → Llama). A/B routing for cost experiments without breaking quality.
Tools we reach for
Not exhaustiveMore in AI Systems Building
Core overview →Personal AI Assistance
A principal-grade assistant across every channel you use.
Business Operations Manager
A multi-agent team that runs a business function 24/7.
AI Software Developer
Agent harnesses that write, review, and ship code.
RAG + Knowledge Systems
Retrieval-augmented chat over a real corpus, with citations.
Messaging Agents
WhatsApp, Telegram, Discord, Slack, iMessage, and web-chat bots.
Agent Orchestration Platform
A fleet of specialised agents, one bridge across your messaging apps.
Real-Time Voice Agents
Live phone and browser-voice agents with streaming and barge-in.
Custom AI Workflows
Document understanding, autonomous loops, extraction, intelligence.
Frequently asked
4 questionsWhy do I need AI evals?
LLMs are non-deterministic. Without evals, a model upgrade or prompt change can silently regress on edge cases your users hit daily. Evals turn "feels worse" into a measurable score and let you ship model changes with confidence.
How do you catch regressions when models or prompts change?
A versioned eval suite runs on every prompt or model change — golden datasets, LLM-as-judge scoring, and behavioral assertions. CI gates merges on score delta. New failures get reviewed before shipping.
Self-hosted (Langfuse) or hosted (LangSmith, Braintrust)?
Depends on data sensitivity and budget. Self-hosted Langfuse on your own infra for sensitive data and zero per-trace fees. Hosted LangSmith or Braintrust when you want zero ops and powerful analytics out of the box.
How long to set up an eval harness?
A working v1 with golden dataset, baseline metrics, and CI integration ships in 1–2 weeks. Deeper LLM-as-judge rubrics, drift detection, and prod sampling come in the next 2–4 weeks.
Sounds like the bucket you’re in?
Tell me what you’re trying to build. I’ll send a written proposal within 48 hours of our discovery call.