At KeyValue, we build and deploy AI agents in real production systems. Over the past 25+ agent deployments, we kept seeing the same failures repeat: agents break silently, behavior shifts after small changes, costs spike without warning, and the “why” is hard to pin down.

Once agents went live, questions showed up every single day.

What path did it take to reach that answer?
What did the model actually see and return?
Why did latency suddenly increase?
Why did costs spike overnight?
Did it call the right tools—and follow the right steps?
Can we see all of this clearly without exposing sensitive data?

We went looking for observability tools to help us answer these questions. What we found were pieces: logs in one place, traces in another, metrics somewhere else. Useful—but not built for agent workflows where decisions, tool calls, prompts, and outcomes all matter.

None of them matched the standards we expect as developers who care deeply about how agents behave in production.

We didn't want to compromise.

So we built what we wanted as developers: one place to evaluate, trace, and monitor every decision your agents make.

What Netra is

Netra is a platform built for agentic systems—combining observability, evaluation, and simulation in one place. It helps teams:

Observe agent runs end-to-end (LLM inputs/outputs, decision paths, tool calls, latency, cost)
Evaluate behavior with measurable signals (so releases aren’t gut-driven)
Simulate real-world scenarios and personas to catch failures before users see them

All through a single, customizable dashboard that fits naturally into how developers work.

Privacy by design

Privacy is foundational to Netra, not an afterthought. With strong data controls, teams can observe and evaluate agents without exposing sensitive information or breaking user trust.