Netra

Real-time alerts on cost spikes, latency, quality drops and safety violations, routed to the channel your team already watches.

Run pre-built or custom evaluators on datasets, experiments, or live traffic. Grade every change before it ships.

Netra automatically clusters production sessions by intent, outcome, failure mode, and cost profile to address systemic issues.

Trace every LLM call, tool execution, and sub-agent handoff with structured, replayable traces in production.

AI voice agent testing explained: simulate real callers, score how your agent hears, thinks and speaks, and catch what transcripts miss.

Articles by Anupam Sekhar C on the Netra blog.

Articles by Arun Sunny on the Netra blog.

Insights from people who've actually built AI agents

Free, Pro ($39/month) and Custom plans for AI agent observability, evaluation and simulation. No credit card needed to start.

Agent OPFOR covers all four OWASP suites with 400+ attack patterns. DeepTeam offers 50+ research backed vulnerabilities. Compare both tools here.

Agent OPFOR vs Promptfoo: open-source AI red teaming for agents & MCP servers — OWASP coverage, CI/CD depth, and vendor independence compared.

LangSmith debugs agent traces, while Netra adds tenant-aware observability, auto-evaluation, and multi-turn simulation for production AI agents.

The Netra team deployed over 25 AI agents before creating the platform, detailing their approach and lessons learned.

Netra offers demos, enterprise and on-premise deployment options, and contact via sales@getnetra.ai.

The platform to evaluate, trace, and monitor every decision your agents make. Observability, evaluation, simulation and red teaming for production AI agents.

Netra’s privacy policy describes how it collects, uses, and protects user data, including security practices, compliance standards, and user rights.

Netra’s Terms of Service outline account eligibility, subscription terms, data rights, security practices, and legal provisions.

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Netra.ai outlines methods for testing AI voice agents by simulating real callers to evaluate how the agent hears, processes, and responds. The guide highlights gaps in traditional transcript-based evaluations and emphasizes assessing the agent’s full interaction cycle. Testing focuses on identifying errors transcripts overlook, such as misinterpreted speech or incorrect responses.

Oct 2026NetraNews article

AgentOPFOR enhances AI agent security by employing trace-aware red teaming to uncover vulnerabilities in tools, APIs, models, and MCP servers. The approach systematically identifies weaknesses in AI systems by simulating adversarial interactions. This method ensures robust security testing for autonomous AI agents.

Aug 2026NetraNews article

Netra.ai releases **Agent OPFOR**, an open-source toolkit for adversary emulation. It tests AI agents, LLM applications, and MCP servers against all OWASP security standards. The project aims to strengthen defenses by simulating real-world attack scenarios.

Jul 2026NetraNews article