
Your agents fail, but your team can't consistently determine where or why.
Similar requests produce different results, and you don't have a reliable baseline for improvement.
Token usage, model selection, and reasoning depth are increasing costs without clear controls.
You need monitoring, alerts, playbooks, and internal processes your team can operate after delivery.
A three-month engineering engagement that instruments your agent system end to end, identifies and fixes the root causes of failures, and delivers production monitoring, cost controls, and operational playbooks.
Teams with AI agents already built or moving toward production that need better visibility, reliability, consistency, or cost control.
No. The engagement is designed to work around your existing architecture and supports common orchestration and observability frameworks including LangGraph, LangChain, PydanticAI, Langfuse, LangSmith, OpenTelemetry, and custom frameworks.
The core engagement runs for three months across three continuous phases: Observability & Attribution, Root Cause & Optimization, and Monitoring & Ownership.
One dedicated Senior AI/ML Engineer is embedded across all three phases, from architecture audit and instrumentation through optimization and handover.
All engagement artifacts are owned by the client at handover. That includes the delivered tracing, monitoring, dashboards, alerts, optimization work, documentation, and operational playbooks.
Start within two weeks of a signed SOW.