I will fix ai agents, debug llm apps, ai evals, llm observability

A complete evaluation infrastructure: offline test suites (catch bugs pre-launch), online monitoring (track live performance), scoring logic (measure quality automatically), and a production feedback loop that turns real user failures into better test cases.

Models fail silently. Evals catch hallucinations, PII leaks, cost spikes, and edge-case failures before users see them. You'll ship safer and faster.

Both. Expect 30–70% reduction in critical failures once we deploy guardrails + evaluation gates. We fix problems, not just report them.

OpenAI, Claude, Qwen, OpenRouter, LangChain, LangGraph, LlamaIndex, custom agents—plus OpenTelemetry-style, Weights and Biases, Braintrust.dev tracing for debugging.

Modern AI systems aren't just prompts—they're agents with tools, multi-step reasoning, and dynamic context. We evaluate the entire system: your prompts, tool definitions, tool outputs, data quality, and agent behavior. That's where 80%+ of your tokens (and problems) actually live.

Three signs: (1) You can ship new AI models in under 24 hours with confidence. (2) User complaints turn into test cases instantly. (3) You use evals offensively—to predict which features will work when better models drop—not just defensively to catch regressions.

Faithfulness (does it follow instructions?), factuality (is it accurate?), task success (did it complete the job?), completeness (did it miss anything?), toxicity, PII leaks, latency, cost per task, and regression detection across versions.

Three sources: (1) Curated gold-standard examples from your domain experts. (2) Synthetic test cases we generate for edge cases. (3) Real production logs—especially failures—fed back into the test suite. The best datasets are living, not static.

Both. Code-based scoring for clear-cut rules (Did it extract the right field? Did it call the right API?). LLM-as-a-judge for nuanced quality (Is this summary helpful? Is the tone appropriate?). We combine approaches based on what you're measuring.

Week 1: Catch a critical bug before launch (prevents customer escalation). Month 1: Cut debugging time by 40%+ with trace graphs showing exactly where agents fail. Month 3: Ship new model updates in days instead of weeks, beating competitors to market.