How Axari builds reliable AI agents with Netra
Open source red teaming
Agent OPFOR
584
Make your text, voice, image and video AI agents reliable
The single platform to trace, evaluate, simulate, and security test your AI agents from development to production.
Trusted by teams building and deploying AI agents
How teams ship reliable AI agents with Netra
Real words from teams building and running AI agents with Netra.
How Pencil watches every agent, every tenant
Netra turned our ad-agent stack from a black box into a clear control center. We can see what every agent is doing, for every tenant, in real time, and Netra now processes over 1M spans every day for Pencil. Issues that once took hours to debug now take minutes.
How Aartha AI ships features faster with Netra
Netra has honestly changed the speed of our shipping. Being able to evaluate and trust what our agents produce before anything goes out has cut down a lot of back-and-forth and helped us get features to market much faster.
How Skydda explains every agent decision
For security workflows, explainability isn't optional. Netra let us show exactly how our agents investigate — every decision, tool call, and LLM exchange, giving CISOs the confidence they need.
Built for AI agents at production scale.
Observability
1B+
Spans processed per month
Every prompt, tool call, retrieval and model response — captured and replayable.
Evaluation
1M+
Evaluations run
Offline against the traces that broke, and online against live traffic.
Resolution
95%
Reduction in mean time to resolution
From the alert straight to the span that broke, instead of grepping logs.
Efficiency
75%
Reduction in AI spend
Token, model and retry cost attributed per agent, per route, per release.
Reliability
99.99%
Platform uptime
Ingest that keeps pace with production traffic, at production volume.
Know how your agent behaves, prove it works before release, and catch issues before users do.
Session tracesupport agent · 892 ms
POST /chat
200
retrieve_docs
200
rerank → 0 hits
200
llm.generate
200
Silent failureAnswered with no context. The log says 200.
Know how your agent behaves
Trace every prompt, model call, tool use, and retrieval across your agent's execution, including the failures that still return 200.
Release gate wasnow
frustrated customer
0.94
ambiguous request
0.88
prompt injection
0.96
tool unavailable
0.91
Gate passedInjection 0.41 → 0.96 across 240 personas, no regressions.
Prove it works before release
Evaluate every change against the traces that broke, then simulate real users, edge cases, and multi-turn conversations before you ship.
Intent clustermulti-turn troubleshooting
Alert → SlackTask success 0.64, caught 14 minutes after 4.2 shipped.
Catch issues before users do
Get alerted when quality drops, behavior drifts, costs spike, or new failure patterns emerge across production conversations.
Prompt Management
Shape how it behaves
Every agent starts with instructions. Write them, test them, change them, and always know what each change did.
- Know which version shipped, and who changed it
- Trace any answer back to the prompt that produced it
- Fix a bad prompt in minutes, without a deploy
Observability
See how it works
Trace every model call, tool use, retrieval, and handoff behind an AI agent response.
- Find where a run went wrong, not just that it did
- Works with your stack as it is, in a few lines of code
- See what every answer costs in tokens, time and money
Evaluation
Test before you ship
Decide what a good answer looks like, then hold every response to it.
- Catch regressions in testing, not in your inbox
- Start scoring on day one with evaluators that already exist
- Turn a production failure into a test you never fail twice
Simulation
Rehearse the real conversation
Expectations catch the answer. Conversations catch the behavior. Put the agent in front of realistic users before real ones arrive.
- See how it handles a difficult user, not a scripted one
- Know whether it reached the goal, not just answered well
- Run a month of conversations overnight
Red Teaming
Find the breaking points
Someone will push the agent past its limits. Better that it is you.
- Find injection, tool abuse and data leaks before an attacker does
- Reproduce any finding from the trace that caused it
- Walk into a security review with the evidence in hand
Online Evaluation
Keep checking quality
Continuously evaluate and score real agent conversations in production, so quality doesn't become a blind spot after launch.
- Catch the failures that never show up in testing
- See which agent, route or customer is actually struggling
- Feed live failures straight back into your test set
Agent Insights
See what is changing
One conversation tells you what happened. A million tell you what is happening.
- Learn what users keep asking for, and what they never get
- Spot drift before it turns into complaints
- Prove a new version made things better, cluster by cluster
Alerts
Know when to act
You should not hear it from a customer first.
- Hear about quality and cost problems while they are still small
- Route every alert to the team that owns it
- Land in the session that raised it, in one click
Available in shared cloud, dedicated cloud, or on-prem deployments.
Works with your entire AI stack
OpenTelemetry-native tracing. Instrument once, keep your existing stack.
Data & Models
LLM Providers
Vector Databases
Frameworks and Tools
AI Frameworks & Orchestration
Database Clients
Infrastructure
Data Stores
Cloud Observability
STT & TTS Frameworks
Start improving your agents in under 5 minutes.
Paste one prompt into your coding agent
Install the Netra skills from github.com/KeyValueSoftwareSystems/netra-skills and use them to add tracing to this application with Netra, following best practices.
Works with Claude Code, Cursor, Codex, and any skills-ready agent.
Start with one line
pip install netra-sdk
Add three lines to your code
from netra import Netra
# Initialize with default settings
Netra.init(app_name="Your application name")Start with one line
npm install netra-sdk
Add three lines to your code
import { Netra } from "netra-sdk";
// Initialize with default settings
Netra.init({ appName: "Your application name" });Security
Secure by design. Safe by default.
Everything an enterprise needs to run Netra at scale.
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Support SLA
Contractual uptime and response-time guarantees, with capacity reserved for your volume.
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Dedicated deployment support
A dedicated engineer embedded in your team to get you live in a week.
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SSO, OAuth and RBAC
Enterprise sign-on, OAuth2 for secure integrations, and granular access controls.
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Scalable infrastructure
Ingest millions of traces and run simulations in parallel, in shared cloud, dedicated cloud or on-prem.
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AI guardrails
Built-in guardrail checks catch hallucinations, PII leaks and policy breaks before your users do.
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SOC 2, HIPAA and GDPR compliant
Build agents that meet the standards your business runs on.
Frequently Asked Questions
Everything You Need to Know About Netra
Get clear answers, fast — talk to our team and get tailored insights for your business in just a few minutes.
What is Netra?
Netra is an end-to-end, agent-centric platform for the full AI agent development lifecycle. It covers prompt management, observability, evaluation, simulation, and red teaming. In production, online evaluations and agent insights keep your agents on track.
What is an AI agent reliability platform?
An AI agent reliability platform helps teams test, monitor, and improve AI agents before and after deployment. It combines AI agent observability, evaluation, simulation, and security testing to catch failures and prevent performance issues in production. Netra brings all of this together in one platform for text, image, voice, and video AI agents.
What is AI agent observability?
AI agent observability is tracking how an agent behaves during execution. It gives you visibility into LLM calls, tool usage, decision-making, latency, token consumption, and costs. Netra captures complete execution traces, so teams can find failures and monitor performance across sessions, users, and tenants.
How do you evaluate AI agents?
AI agent evaluation measures how accurately, safely, and reliably an agent completes tasks. It scores response accuracy, goal completion, tool usage, and safety against defined criteria. Netra runs automated AI agent evaluations on test datasets, compares performance across agent versions, and scores live interactions through online evaluations.
How do you test AI agents before production?
You can test AI agents before production with multi-turn simulations, automated evaluations, and AI red teaming. Together, they surface failures in real-world scenarios, including unexpected user behavior, prompt injection, tool misuse, and data leakage. Netra simulates realistic conversations, evaluates each response, and links failures to execution traces, so teams can fix issues before deployment.
How does Netra help with AI agent debugging?
Netra traces each agent request from start to finish. Every trace breaks into spans that show latency, cost, and token usage for each LLM call, tool invocation, and decision point. An MCP server also lets you query and debug traces from inside your IDE.
Can Netra test and evaluate AI voice agents?
Yes. Netra provides AI voice agent testing through realistic call simulations, automated voice evaluations, and production monitoring. Simulations cover different caller personas, interruptions, and conversation scenarios to evaluate speech recognition, response quality, conversational behavior, and speech synthesis. Netra supports LiveKit, WebSocket-based voice agents, and Vapi for simulation, with turn-by-turn tracing and evaluations for live calls.
How does Netra monitor AI agents in production?
Netra monitors AI agents in production through real-time observability, online evaluations, and automated alerts. It tracks execution, response quality, latency, token usage, and costs across live interactions. It also detects performance drift and user intent patterns, and links each issue to its execution trace, so failures get resolved faster.
How is Netra different from an LLM observability platform?
Traditional LLM observability platforms track model calls, prompts, responses, latency, and costs. Netra goes beyond LLM monitoring to deliver end-to-end AI agent reliability testing and monitoring. It combines multi-turn simulation, automated evaluations, voice agent testing, AI red teaming, production tracing, and real-time alerts. Teams catch failures before deployment and keep improving agents in production.
What LLM providers and frameworks does Netra support?
Netra supports 14+ LLM providers, including OpenAI, Anthropic Claude, Google Gemini, AWS Bedrock, and Mistral. It also supports 12+ frameworks, including LangChain, LlamaIndex, and CrewAI. It is built on OpenTelemetry standards.
What deployment models does Netra support?
Netra is available as a fully managed SaaS (cloud) service. Teams that need their data to stay within their own environment can choose an on-premise or self-hosted deployment.
Is Netra secure and compliant?
Yes. Netra is SOC 2 Type II certified and HIPAA and GDPR compliant. It provides tenant data isolation, encryption in transit and at rest, PII detection and masking, and US and EU data residency with no cross-region sharing.
How do I get started with Netra?
Install the SDK with pip install netra-sdk for Python or npm install netra-sdk for TypeScript. Initialize it with your API key, and tracing begins immediately. The Quick Start guide walks you through the setup.
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Turn 10‑hour investigations into 10‑minute fixes
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