About Noveum
Reliability infrastructure for the agent era.
Noveum helps teams understand and improve production agents by tracing instrumented runs, evaluating them with purpose-built scorers, testing voice and chat scenarios, and turning failure evidence into recommendations engineers can verify.
Open-source Python SDK · SOC 2 Type II in progress · scoped enterprise deployment options
5
Products, one loop
112
Published scorers
15
Scorer categories
4-line
SDK integration
01 - Why we started
Shipping an agent is easy. Keeping it reliable is not.
Noveum started with a frustration every team building on LLMs eventually hits: getting an agent to work in a demo takes an afternoon; keeping it reliable in production takes forever - and the tools meant to help were built for a world that no longer exists.
Traditional observability assumes deterministic software: same input, same output, a stack trace when something breaks. AI agents are the opposite. They reason across many steps, call tools and other agents, and “wrong” is rarely a crash - it's a confident, plausible-looking answer that quietly fails a user. APM dashboards can't see that. Logs can't explain it. And nobody can read enough traces by hand to stay ahead of it.
So we set out to build a reliability layer for AI-native systems - one that treats evaluation as a first-class signal, reconstructs real scenarios, and turns raw failures into evidence teams can act on. Noveum connects tracing, evaluation, synthetic testing, guardrail configuration, and ranked improvement recommendations in one workflow.
02 - What we believe
The principles the product is built on.
A few convictions shape every decision we make - in the architecture, the roadmap, and the words on this page.
AI-native, not retrofitted
We didn't bolt AI onto an APM tool. Instrumented tracing, configured scoring, synthetic testing, and cost or rate policy controls were designed for non-deterministic, multi-step, tool-using agents.
Evidence over vibes
A recommendation is only useful when the evidence behind it is visible. Noveum links findings to affected items, traces, scorer results, confidence, and expected impact so teams can verify changes themselves.
Reliability is a loop
Dashboards tell you something changed. A dependable workflow connects detection, diagnosis, implementation, and reevaluation so teams can measure whether a change actually helped.
Your data stays yours
Choose managed cloud or scope in-VPC, on-premise, or BYO ClickHouse options for a qualifying enterprise engagement. SOC 2 Type II is in progress; review data flows and GDPR requirements for the selected architecture.
Open by default
Our Python tracing SDK is open source and integrates in a few lines. We'd rather earn trust through code you can read than lock-in you can't escape.
Honest about what we measure
A scorer result, a synthetic run, and a production trace answer different questions. We label each signal by what it actually measured and keep recommendations separate from changes your team has verified.
03 - What we're building
One closed loop for agent reliability.
noveum-trace, NovaEval, NovaPilot, and NovaSynth form an evidence loop around a team-owned change. NovaGuard stores cost and rate policies for a connected application or gateway to enforce.
- 1
Trace
Capture instrumented LLM calls, tools, retrievals, and agent steps as a structured trace.
- 2
Evaluate
Choose compatible checks from 112 published scorers; 76 model-backed scorers can include judge reasoning.
- 3
Recommend
Turn evaluated failures into ranked recommendations backed by scorer evidence and affected items.
- 4
Implement
Your team reviews the evidence, makes a candidate change, and owns its code review and deployment.
- 5
Simulate and re-evaluate
Exercise explicit personas and scenarios, then compare new evaluation evidence before and after release.
NovaGuard (beta) sits alongside this loop. It stores cost-cap and request or token rate-limit policies for a connected application or gateway to fetch and enforce.
04 - Where we are
Built in the open, proven in production.
We ship fast and we ship honestly. Here's the foundation Noveum already runs on.
Open-source Python SDK
noveum-trace for Python - tracing for LangChain, LangGraph, LiveKit, Pipecat, and CrewAI in a few lines of code.
112 published scorers
A library across 15 categories: 82 Premium, 30 Free, 76 model-backed, and 36 dedicated voice and audio scorers.
Enterprise-ready
SOC 2 Type II is in progress. Enterprise teams can scope GDPR requirements and on-premise, in-VPC, or BYO ClickHouse options during deployment review.
In production at scale
Platforms like MyOperator use Noveum with production AI voice agents - integrated in four lines of code and reviewed with trace and evaluation evidence.
Build agents you can trust.
Start tracing in minutes, or talk to us about deploying Noveum in your own environment. We're also hiring the people who'll build the next part of the loop.