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 need, what already exists, and the useful outcome. You will receive the next step by email.