Point Agent Assurance at an agent you own. It derives the suite from your code, invokes the agent for real, and reports what it could not verify. Automate Browser Flows from your Terminal with Kane CLI Trusted by 3M+ users globally at " We have tripled our tests and are now executing tests in less than 2 hours with 78% Faster Test Execution " Hrishi Potdar , Quality Engineering Architect " We figured out a more efficient way to monitor system health and resolve failures earlier in lower environments. " Tenny , Engineering Operations Lead " TestMu AI has significantly boosted our testing speed, is easy to implement, and provides exceptional support. " Nicholas Paulsen , Senior Quality Engineer " With 70% faster test execution, TestMu AI helped us achieve faster time-to-market and enhanced CX. " Daniel de Bruijn , Quality Assurance Automation Engineer Agent Regression Testing, Built on Two Premises Agent Assurance grades what your agent actually did, and publishes the share of it nobody could see. Grade the effect, not the account An agent's account of what it did is the weakest evidence available about what it did. It is the one party with a reason to be wrong. Publish the blind spot Anything Agent Assurance could not verify is reported as unverifiable. Never a quiet pass, never a guessed fail, never folded into the pass rate. No tests to write It reads the codebase and derives functional, non-functional, and adversarial scenarios for the agent it finds there. Adversarial by default Prompt injection, instruction override, and tool misuse are a first-class scenario family, not an add-on you configure. Runs in your CI Headless subcommands and exit codes that separate a broken agent from a harness that never got a look at it. End to End Agent Testing, From Codebase to Verdict Four phases, from a folder you own to a verdict you can take to a release meeting. Your Codebase Is the Test Plan Manifests, prompts, tool tables, and MCP servers. Where an agent declares nothing, it says so. Deterministic discovery reads manifests and tool tables Connects to MCP servers and asks what tools exist Undeclared fields come back unknown, never guessed A Suite You Did Not Have to Write Functional, non-functional, and adversarial scenarios, each carrying its own gradable criteria. Scenarios derived from code, not authored by you Adversarial family covers injection and tool misuse The criterion is the unit of judgement, not the test Graded on Effect, Not on Claims The agent is invoked for real, and each criterion is judged against what actually changed. Watches files, artifacts, and every tool call made Checks calls against the agent's declared tool surface Judges verify read-only and change nothing Two Numbers, Not One Per-criterion verdicts with quoted evidence, plus the share of criteria nobody could check. Pass, fail, and unable to verify are three verdicts Unverifiable is excluded from the pass rate Newly failing, newly fixed, and flaky are separated Continuous Agent Testing, Every Type in One Run You do not pick a test type up front. One generated suite covers all of these, and each criterion is graded against the evidence it left behind. Agent Functional Testing Derived from your codebase, then graded per criterion against what changed, not against the reply. Agent Regression Testing Every run is diffed against the last. Newly failing, newly fixed, and flaky are reported apart. Agent Smoke Testing The invoke profile is probed once with a trivial goal before a suite spends anything. Agent Automation Testing Discovery reads the agent, generation writes the scenarios, judges grade every criterion. Continuous Agent Testing Headless subcommands drop into your pipeline. Gate the build on exit 2, never on the gap. Adversarial Agent Testing Prompt injection, instruction override, and tool misuse are generated as a first-class family. Why 1 and 2 Are Different Numbers A build should stop for either, but only one of them is a finding about your agent. Exit 1 means the harness never got a look. This is the exit-code expression of the same honesty rule, and it is why nothing unverifiable ever fails your build. Fail the build on 2. Treat 1 as infrastructure and surface it loudly rather than swallowing it. Measure the assurance gap for a few weeks before you gate on it at all, then set a threshold you have earned. What Each Approach Actually Proves Most tools grade the transcript. Agent Assurance grades the effect, and says what it could not see. Agent Assurance Eval frameworks LLM observability Who writes the scenarios Derived from your codebase Each criterion, against evidence The final response Spans after the fact Files, artifacts, tool calls Transcript text Traces and logs Tool calls checked against the declared surface Recorded, not judged Verdicts available Pass, fail, unable to verify What could not be proved Reported as a number Silently folded into the score Adversarial coverage Generated as a family Only what you author Runs before release No, production only Agent Assurance Who writes the scenarios Derived from your codebase Each criterion, against evidence Files, artifacts, tool calls Tool calls checked against the declared surface Verdicts available Pass, fail, unable to verify

What could not be proved Reported as a number Adversarial coverage Generated as a family Runs before release Eval frameworks Who writes the scenarios The final response Transcript text Tool calls checked against the declared surface Verdicts available What could not be proved Silently folded into the score Adversarial coverage Only what you author Runs before release LLM observability Who writes the scenarios Spans after the fact Traces and logs Tool calls checked against the declared surface Recorded, not judged Verdicts available What could not be proved Adversarial coverage Runs before release No, production only Built for Every Layer of Agent Assurance Engineers building agents A generated suite in minutes without writing tests, plus a run-over-run diff that separates a real regression from a flaky scenario. QA and test engineering A unit of coverage that survives scrutiny. Criteria proved against evidence, and an explicit account of everything that was not checked. Engineering leaders Two numbers instead of one. The pass rate, and the assurance gap that tells you how much of that pass rate anybody actually observed. Platform and DevEx teams One assurance step that drops into CI for every agent your product teams ship, replacing a homemade eval script per repository. Security and risk Prompt injection and tool misuse become a repeatable test, with an exit code that fires when the agent is compromised. Compliance and audit Per-criterion evidence retained as plain files, alongside an explicit record of what could not be verified on each run. Success Stories of TestMu AI (Formerly LambdaTest) reduction in test execution time “HyperExecute is a highly reliable test execution platform and has excellent customer support.” Sagar Uday Kumar Sr. Engineering Manager Three Surfaces. One Verdict. Work in the terminal, gate the build in CI, and hand an auditor a sealed evidence pack. An interactive session for the engineer who owns the agent. Anything that spends shows its plan and asks first, so a suite never surprises you with its cost. Your CI pipeline Headless subcommands with exit codes that distinguish a broken agent from a harness that could not test it. Unverifiable results are reported, never used to fail a build. A sealed evidence pack Requests, responses, per-criterion verdicts and artifacts for a run, plus the record of what was not verified. The artifact a security review actually asks for. Some Love from our Customers As Best Egg expanded its product offerings and entered new markets, we knew our old testing infrastructure couldn’t keep up. With support from Tenny Agustin, our Engineering Operations Lead, we modernized our approach with @ testmuai see more > Excited to Share My Learning Journey with Kane AI & Lambda Tool! I'm pleased to announce that I've recently gained hands-on experience exploring Kane AI through the Lambda Tool and it’s been a fantastic journey of upskilling! see more > See how @ testmuai is #Futureready to enable blazing-fast test orchestration seamlessly integrated with organizations' existing CI/CD platforms, using #Microsoft Azure. Microsoft India More Reasons to Love TestMu AI (formerly LambdaTest) See how TestMu AI speeds up your testing with AI-native authoring, faster execution, and deeper test insights across web, mobile, and AI applications. TestMu AI Named a Challenger in the 2025 Gartner® Magic Quadrant™ TestMu AI recognized in The Forrester Wave™: Autonomous Testing Platforms, Q4 2025 TestMu AI is #1 choice for SMBs and Enterprises across the globe. Enterprise-Grade Security We safeguard your data and AI systems with global security, privacy, responsible AI, and ESG standards. Works where you work, 120+ integrations with the tools your team relies on. Frequently asked questions What is agent assurance? How is this different from an eval framework? What is the assurance gap? Why report what you could not check? Does that not look bad? Do I have to write the tests? Does it work with non-deterministic agents? Will it change my data? What kinds of agents does it cover? Can I use this for n8n agent testing? Can I gate my build on it? Do I need my own model API keys? How does this relate to Kane CLI and KaneAI? How do I get access? TestMu AI for Enterprise Get access to solutions built on Enterprise grade security, privacy, & compliance Advanced access controls Advanced data retention rules Advanced Local Testing Premium Support options Early access to beta features Private Slack Channel Unlimited Manual Accessibility DevTools Tests Advanced access controls Advanced data retention rules Advanced Local Testing Premium Support options Early access to beta features Private Slack Channel Unlimited Manual Accessibility DevTools Tests