Most AI agents don’t fail because they’re dumb. They fail because there’s no truth layer. That’s what we built with Themis — now live on the Tessl Skill Registry 👇 tessl.io/registry/vitron-ai/… ⚖️ Turn outputs into verdicts 🧠 Replace prompts with testable logic 🔁 Make agents repeatable deterministic This isn’t just “better prompting.” It’s a shift from generation → validation. Your agents shouldn’t just write code… they should prove it works. If you’re building with AI, this is the missing layer. Install. Evaluate. Ship truth. #AI #Agents #Testing #E2E #unittesting #DevTools #OpenSource #Tessl #agenttesting #aiagentskills

6 months of manual testing. Replaced in 30 minutes. Jun-shuo (Lance) Liu , a research engineer at Columbia University, was stuck in a cycle most AI agent developers know well - designing test cases by hand, reading through every conversation, writing bug reports, and starting over with every update. He tried ArkSim. Here's what happened: → Test report time: 2–3 days → 30 minutes → Iteration cycle: 1–2 weeks → 1–2 days → Accuracy: 80% → 90% in one week But the biggest unlock wasn't speed. It was visibility. ArkSim surfaced a tool selection bug he'd been living with for months. This type of bugs was invisible to manual review, caught in a single run. He wrote up the full story: arklex.ai/home/blogs/from-6-… #AIAgent #AIEval #AgentTesting

ArkSim - Know how your agent performs before it goes live - github.com/arklexai/arksim conversations between LLM-powered users and your agent, then evaluates performance across built-in and custom metrics. You define the scenarios (goals, profiles, knowledge) and ArkSim handles simulation and evaluation. Works with any agent that exposes a Chat Completions API or A2A protocol endpoint. #AISecurity #AIAgents #LLMEvaluation #AgentTesting #AIObservability

Speed. Stability. Insights. The December product updates for TestMu AI are live 🔗 bit.ly/4a6iUhW We’ve rolled out significant enhancements designed to help you ship faster in Q1. Now, teams can orchestrate JMeter executions, run stable Chrome tests, generate Lighthouse performance reports, and manage KaneAI projects more efficiently. Read the full update now! #TestMuAI #SoftwareTesting #AIAgent #AgentTesting #TestAutomation

AI agents are dynamic, so why is your testing static? To truly test an AI, you need an AI. Here's an Agent-to-Agent Testing Platform by LambdaTest 🔗 bit.ly/4q3Bopm, where you automate the generation of diverse scenarios to test your Voice, Chat, and Calling agents against the metrics that matter most: 🚫 Bias ☣️ Toxicity 😵‍💫 Hallucinations ⚠️ Latency 👥 User Satisfaction, etc By leveraging a variety of user personas, we help you ensure your AI performs effectively for everyone, every time. Accelerate your release cycle today. 🚀 #LambdaTestYourApps #AIAgents #AgentTesting #AgentToAgent #QualityEngineering

Your AI agents are getting smarter! But are you testing them smart enough?🤔 Meet the new way to validate AI chat, voice, hybrid, and phone caller agents with agents that test other agents. Our Agent-to-Agent Testing platform 🔗 bit.ly/4hUqZtm automatically generates diverse, real-world scenarios your AI systems will actually face: ➡️ Dynamic chat conversations ➡️ Voice-driven flows ➡️ Hybrid multimodal interactions ➡️ Full caller simulations No manual scripting. No assumed edge cases. Just intelligent, automated scenario generation that stress-tests your AI like a real user would at scale. #LambdaTestYourApps #AIAgent #AgentTesting #AI #QualityAssurance

"With the persistence of a river carving a new path through stone." Manual testing of AI agents is a time taking. Rogue changes the game. An open source framework where one agent tests another automatically. #ThoughtForTheDay #agent #agenttesting

Why AI agents forget - and how Context Engineering solves it AI agents fail when context isn’t structured or retrieved properly, leading to errors, outdated information, or reasoning drift. In this blog, @saikrisv & @srinivasanskr, Directors of Engineering at LambdaTest, break down the 4 context failure modes - poisoning, distraction, confusion, clash and show how the WRITE and SELECT pillars manage memory and retrieval for reliable, task-relevant outputs along with real-world examples. Read now🔗 bit.ly/4hgis3L #AI #AIAgent #ContextEngineering #AgentTesting #LambdaTestYourApps

AI is evolving fast. Testing needs to evolve faster.⚡ With LambdaTest’s Agent-to-Agent Testing, we’re setting a new benchmark: 😎Agents testing agents through a multi-agent system 🌎Intelligent real-world scenario generation that covers the known, unknowns, and edge cases ⚡Continuous, scalable validation with parallel execution for instant feedback 🔒Broader coverage, including the hardest edge cases This isn’t just another tool. It’s the future of how AI agents get tested. Learn More👉 bit.ly/3V8p0Hi #AI #AgentTesting #MultiAgentSystems #LambdaTest

PentestJudge: Judging Agent Behavior Against Operational Requirements -arxiv.org/abs/2508.02921 by @dreadnode Introducing PentestJudge, an LLM-as-judge system for evaluating the operations of pentesting agents. The scores are compared to human domain experts as a ground-truth reference, allowing us to compare their relative performance with standard binary classification metrics, such as F1 scores. Inspired by OpenAI's work with PaperBench, "PentestJudge: Judging Agent Behavior Against Operational Requirements" explores: ➡️ Model performance vs. human experts (seeing strong performance from Claude and Kimi) ➡️ Cost analysis of the different models evaluated vs. human experts ➡️ The different failure modes observed in the judge, with an emphasis on shallow model tool calling capabilities ➡️ Future work leveraging the output of these judge systems as a reward signal for difficult-to-verify domains within security using techniques like GRPO for model training Authors: @shncldwll, @0xdab0, @VincentAbruzzo, @moo_hax, and Michael Kouremetis. #PentestJudge #PenTestAI #AISecurity #AIJudging #SecurityAI #AIModels #ModelTesting #CyberAI #AIResearch #AIExperts #ClaudeAI #KimiAI #AITraining #ToolCalling #ModelScores #AIAgents #CostAnalysis #FailureModes #RewardSignals #AgentTesting #AICompare #Benchmarks #AIJudge #PentestJudge #LLMSecurity

I’ve been in Software test engineering for over a decade. 🧪 I’ve seen test automation evolve from: ➡️ Basic shell/Python scripts ➡️ Record-and-play tools like selenium IDE, QTP, TestComplete ➡️ Selenium test suites that broke when any UI element changed Then came the shift to code: ✅ pytest for fast backend/API tests ✅ JUnit/TestNG for scalable test suites ✅ We automated assertions, mocked services, tested logic early 🔁 CI/CD became the norm — tests ran on every commit Tests were no longer “extra.” They were essential. Then came the browser revolution: 🚀 Tools like Cypress and Playwright gave us: Parallel runs Automatic waits Reduced flakiness Dev-friendly test flows I still remember the first time I saw 50 UI tests run in under 2 minutes. Magic. ✨ 🌐 Then in terms of performance testing: JMeter ruled for a while Then came k6: modern, scriptable, DevOps-ready We started testing scale, not just correctness. But today? I’m witnessing something wild. 🤖 Agent-vs-Agent Testing is here. One AI agent simulates a user The other is the system under test (AI chatbot, AI voice agent,) LLMs like GPT act as judges, evaluating responses for: Correctness Tone Helpfulness Real intent match We’re no longer asserting status codes or UI clicks. We’re testing language, intent, and experience. 🧠 The test automation stack is no longer just a pyramid. It’s a feedback loop — powered by AI. 🎥 Want to see Agent-vs-Agent testing in action? Check the attached video #QA #TestAutomation #LLM #AgentTesting #Cypress #Playwright #pytest #JMeter #k6 #DevOps #AI #SoftwareTesting #BuildInPublic #TestingCommunity

Not all agents are created equal @recallnet puts them to the ultimate test. 🔥 Open-ended competitions 🧠 Framework-agnostic evaluation ⚔️ Real-time performance battles If your AI can thrive here, it’s ready for anything. #RecallNet #AgentTesting #AICompetition