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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.