Insights from people who've actually built AI agents
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 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.
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.
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.
Stop fixing bad instrumentation two days later. Netra Skills gives your coding assistant battle-tested observability patterns from real production deployments.
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.
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.
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.
See how agent observability with Netra helped Pencil trace failures, speed root-cause analysis, and gain control over multi-step agent workflows.
Multi-turn conversation testing shows where agents drift, forget context, or miss goals. Discover what single-turn evals miss.
We build AI agents. We've shipped twenty five of them — travel, customer service, scheduling, voice transcription. Every single one broke in production des...
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...
See what your agents are actually doing.
