AI Agent Security: How to Red Team Your AI Agents
Learn how to red team AI agents with Netra. Configure automated security tests, run adversarial attacks, detect vulnerabilities, analyze Safety Scores, and improve AI agent security across releases.
Blogs
Insights from people who've actually built AI agents
AI Agent Evaluation vs. Simulation vs. Red Teaming
A comparison of AI agent evaluation, simulation, and red teaming. Learn how each works, when to run it, and why you need all three for AI agent testing.
What Is Voice Agent Simulation, and How Do Simulated Callers Work?
Simulated callers test your voice agent over real audio before customers do. See how they work, how to design them and where they fall short.
Online Evaluation for AI Agents: How to Monitor Production Quality
An AI agent can pass every test before deployment and still fail in production. Learn how online evaluation helps teams monitor real user interactions, detect quality regressions, and continuously improve agent reliability.
Voice Agent Testing Checklist: 15 Tests to Run Before You Go Live
Explore 15 AI voice agent tests to run before launch, from latency and interruptions to accents, noise, tool calls and live monitoring.
Cekura vs Netra: Which Is the Best Cekura Alternative for Voice and AI Agent Testing?
An honest, sourced look at Cekura vs Netra for voice agent testing, monitoring and pricing, and when each one is the better fit.
AI Agent Simulation: A Guide to Multi-Turn Agent Testing
Single-turn tests can miss failures that emerge as conversations evolve. Learn how AI agent simulation uses realistic user goals, behaviours, and personas to test multi-turn interactions, uncover hidden failures, and improve agent reliability before deployment.
What Are the Best AI Voice Agent Testing Tools in 2026? 8 Platforms Compared
Compare the best AI voice agent testing tools in 2026. See how eight platforms simulate, evaluate, and monitor AI voice agents from test to live.
How Do You Test an AI Voice Agent? A Complete Guide to AI Voice Agent Testing (2026)
A voice agent can fail even when its transcript looks fine. This guide covers AI voice agent testing with the Hear, Think, Speak framework: check your agent's performance using simulated callers, real-world noise, and audio-scored evaluators.
AI Agent Evaluation: A Guide to Testing Across Production
AI agent testing shouldn't end at deployment. Learn how offline and online evaluation, regression testing, and production monitoring help teams catch failures, measure quality, and continuously improve AI agent reliability.
AI Agent Observability Best Practices: A Step-by-Step Guide
Learn how AI agent observability helps teams trace LLM calls, tool use, retrievals, latency, token usage, cost, and failures across production agent workflows.
Prompt Management for AI Agents: A How-To Guide on Prompt Versioning, Testing, and Evaluation
Learn how to manage AI agent prompts with versioning, testing, evaluation, deployment controls, and observability using Netra Prompt Studio.
Portkey Alternatives for AI Agent Teams: Netra vs Portkey, Compared
Comparing Netra and Portkey? Portkey handles routing, failover, and caching. Netra handles AI agent evaluation, simulation, and AI red teaming, so you know your agents are correct, not just delivered
Agent OPFOR vs DeepTeam: AI Red-Teaming Tool Comparison (2026)
Comparing Agent OPFOR and DeepTeam for AI red teaming: what each covers, how they differ, and which fits your stack.
Agent OPFOR vs Promptfoo: Which AI Red Teaming Tool Goes Deeper on Agents and MCP
Your AI agent doesn't just talk — it calls tools, holds memory, and reaches into MCP servers. We compare Promptfoo and Agent OPFOR to see which open-source red teaming tool actually catches a real failure before someone else does.
Netra vs Maxim AI: Which AI Agent Observability Platform Is Right for You in 2026
Compare Netra vs Maxim AI for AI agent observability in 2026. Explore how they differ across tracing, evaluations, simulations, prompt management, behavioral analytics, and production reliability to find the right platform for your AI systems.
AI Red Teaming for Agents: Why AgentOPFOR Gets It Right
AI agents can fail in ways traditional security testing misses. See how Agent OPFOR enables trace-aware red teaming across models, tools, APIs, and MCP servers to uncover real agentic risks.
What Is Agent OPFOR? Open-Source AI Agent Red Teaming for LLM Apps and MCP Servers
AI agents can fail in ways their final responses never reveal. Discover how Agent OPFOR uses open-source, trace-aware adversarial testing to uncover vulnerabilities across prompts, tools, memory, APIs, MCP servers, and multi-turn interactions before attackers find them.
Scaling LLM agent evaluation with Netra
A wrong AI output can sound as confident as a right one. Here's how structured evaluation with Netra catches silent regressions before they reach production.
Netra Skills: What they are, why they save hours, and how to get started
Stop fixing bad instrumentation two days later. Netra Skills gives your coding assistant battle-tested observability patterns from real production deployments.
Why AI Agent Evaluation Isn't Optional
AI agents can produce confident but incorrect results without triggering a single error. Learn how structured evaluation, reusable evaluators, production scoring, and automated quality gates help teams detect regressions and improve agent reliability before users encounter the failures.
Netra MCP Explained: How to Debug AI Agents Without Leaving Your IDE
Debugging AI agents usually means bouncing between your IDE, dashboards, and logs to piece together what went wrong. Netra MCP brings that observability context directly into your coding assistant, letting you query live traces and pinpoint failures without ever leaving your editor.
From Agent Tracing to AI Agent Observability: Where LangSmith Stops and Netra Starts
Tracing can explain what happened in a single AI agent run, but it cannot reveal how the system is performing over time. Explore how Netra goes beyond LangSmith-style tracing with continuous evaluation, multi-turn simulation, and tenant-aware observability for reliable production AI agents.
How Agent Observability took Pencil from Black Box to Control Center
See how agent observability with Netra helped Pencil trace failures, speed root-cause analysis, and gain control over multi-step agent workflows.
Beyond Single Turns: What We Learned Running Multi-Agent Simulation at Scale
Multi-turn conversation testing shows where agents drift, forget context, or miss goals. Discover what single-turn evals miss.
Why You Can't Unit Test Your Way to Reliable Agents
We build AI agents. We've shipped twenty five of them — travel, customer service, scheduling, voice transcription. Every single one broke in production des...
What We Learned Shipping 9 AI Products
Last March, our travel AI sent a user to a resort that doesn't exist. They'd asked for a road trip itinerary. The AI confidently included "Sunoutd...
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