Why Tovix
Traditional software fails visibly - exceptions, crashes, wrong output. AI agents fail conversationally. The agent responds. It sounds fluent. It passes your QA checklist. And the customer leaves with their problem unsolved, or worse, with confidently stated wrong information.
Tovix is built to find these failures - before and after production.
↻ Production failures → regression tests, automatically
Real failures from production don't just generate alerts - they become test scenarios you can re-run until the fix sticks. The same conversation that broke in production protects every future launch.
The most dangerous failures are the invisible ones. The agent responds fluently. Latency is fine. The conversation ended without an error code. And the customer left with their problem unsolved - or carrying wrong information they now believe is true.
Traditional QA catches explicit errors. Observability tools flag performance degradation. Neither catches an agent that answered the wrong question with confidence, or one that described your product correctly but never helped the user act on it.
Tovix finds what others miss
Traditional logs and QA show what happened
Tovix shows what broke and why - and turns it into a test so it never reaches production again
Tools like LangSmith, Langfuse, and Arize are built to help engineers debug. They show token counts, latency, retrieval sources, and completion events. They answer “did the agent respond?” not “did the customer get what they needed?”
Tovix starts from the customer outcome and works backward. Every conversation is evaluated against what the user actually wanted to achieve. If the agent responded confidently but the customer left without resolution, Tovix surfaces it.
We analyzed AI agents from 200+ companies. The same failure patterns appear again and again - across industries, models, and use cases. Most teams have no visibility into any of them.
“User asked about renewing a business insurance policy. The agent explained personal auto coverage requirements in detail. Confidently wrong.”
“User said "I want to upgrade today." The agent described Premium features four times. It never initiated the upgrade flow.”
“User asked whether the service covers their zip code. The agent answered twelve questions the user never asked. The original question went unanswered.”
“Agent quoted a policy deductible that had changed six months prior. Stated as fact. No hedge, no caveat, no escalation.”
An agent's job is not to respond - it's to help. Those are not the same thing. A user who asks to upgrade their plan and receives four paragraphs about plan features did not get help. They got a response.
Tovix extracts the user's actual goal from every conversation - not what they literally typed, but what they came to accomplish. It then measures whether the agent closed that loop.
Every conversation has a goal the user arrived with and a result they left with. The gap between those two is where your agent quality lives - and where most teams have no visibility.
Tovix measures whether real goals were met across every conversation at scale. It identifies which interaction patterns correlate with abandonment, escalation, or repeat contacts - and surfaces the highest-priority fixes first.
LLMs do not flag uncertainty - they fill the gap with a confident-sounding answer. When your agent states a wrong policy deductible, quotes an outdated return window, or describes a feature that was deprecated two releases ago, it does so with the same tone as when it's right.
Tovix compares agent claims against what you know to be true. It flags discrepancies, highlights uncertain patterns, and keeps evidence you can act on - whether that means a prompt fix, a knowledge base update, or a human review.
From intent to action, evaluation, and improvement: one loop your team can follow.
AI agents operate at scale in ways humans can't fully review. When something goes wrong - a wrong claim, a missed compliance requirement, a high-risk interaction - teams need to explain what happened and show what they changed.
Tovix keeps a clear, auditable record from detection to fix. Every finding links to the specific conversation that surfaced it. Every resolution is tied to a test that verifies it. When someone asks “how do you know the fix worked?” - you have an answer.
Tovix is designed for the full team that owns agent quality - not just engineers.
Shows which failures matter, turns them into test coverage, and proves the next release is better.
Finds unresolved, abandoned, confusing, or risk-laden interactions - and tells you what to fix first.
Focuses human review on high-risk cases, documents decisions, and monitors whether fixes stick.
Turns real failures into reusable test scenarios and catches regressions before launch.
Not every AI mistake can be fixed with a prompt change. Some conversations carry risk that requires a human to review, decide, and document. The challenge is finding those conversations in a stream of thousands.
Tovix prioritizes the queue. High-risk signals - safety concerns, policy violations, emotionally escalated interactions, uncertain claims - surface automatically. The result is a manageable review workflow, not inbox exhaustion.
Common questions from teams evaluating Tovix.
Native platform tools test whether your agent runs correctly inside their environment. They answer one question: did the agent respond to this prompt? Tovix asks a different question: did the customer actually get what they needed? That is a fundamentally different measurement. Beyond that, native tools are useless the moment you need to compare platforms, evaluate a migration, or run agents built on multiple frameworks. Tovix is platform-agnostic by design. It also closes a loop native tools cannot: when a real customer has a bad experience in production, Tovix turns that failure into a regression test that runs automatically on every future release. That flywheel does not exist in any native tooling.
Observability tools show you what the agent did - token usage, latency, retrieval events. They answer 'did the agent respond?' Tovix evaluates whether the user's goal was actually achieved. The question isn't whether the agent produced a response - it's whether the customer got help. Tovix is outcome-first, not trace-first.
Hand-written tests cover the users you anticipated. Tovix runs your agent against 8 distinct user types, including adversarial, detail-oriented, and security-probing personas, built from how real users actually deviate from happy paths. The failures your team didn't write for are exactly the ones that show up in production. Beyond that, Tovix turns those failures into reusable regression tests automatically, so your test suite grows from real incidents rather than from what your team imagined in advance.
Red-teaming finds edge cases. Tovix finds patterns. We're not looking for the one clever prompt that breaks your agent. We're identifying which failure types fire most often across realistic conversations, ranking them by frequency, and surfacing the single highest-impact fix. The output isn't a list of vulnerabilities. It's a prioritized queue of the behaviors most worth changing, with a specific prompt instruction ready to apply.
You can build the rubric. What takes years to develop is the calibration: knowing what a deflection failure looks like in a billing agent versus a healthcare agent versus a customer support bot. Our failure taxonomy was built by analyzing patterns across 200+ real agent deployments, not by writing assumptions in a spreadsheet. When you build your own eval, you're scoring against a mirror. Your team's prior beliefs about what failure looks like, with no external reference point.
We didn't start from a written rubric. The failure taxonomy came from systematically testing publicly accessible AI agents across 200+ companies, observing where they broke, and labeling those patterns. Your conversation data stays in your isolated environment and is never used to update shared models. The eval reflects what we learned from public agents before you connected anything.
No. Tovix connects to your existing conversation history - whether from your CRM, support platform, or API logs. There's no SDK to install and no instrumentation required. You can be running evaluations within minutes of connecting your data source.
Any conversational AI agent - customer support bots, sales assistants, internal knowledge agents, voice agents with transcripts. If conversations can be exported or streamed, Tovix can evaluate them. Tovix is model-agnostic and works regardless of which LLM or framework your agent uses.
Tovix scores each conversation across multiple dimensions: task completion, factual accuracy, safety, and user experience. Each score is backed by evidence from the transcript - you can see exactly which exchange drove the score. You're not looking at a number; you're looking at an explanation.
Most evaluation tools score individual responses - was this turn polite? Was this answer accurate? Tovix scores the full conversation outcome: did the user achieve what they came to do? A polite, fluent response that doesn't resolve the user's problem scores poorly on task completion. Outcome-first means the customer's result is the ground truth.
Yes - and that's the point. The production-to-test flywheel means failures caught in production become test scenarios you can run against your next agent build before it ships. You can also load historical conversations or synthetic scenarios before your agent goes live. Tovix is designed for both pre-launch testing and continuous production monitoring.
Tovix costs $29 per 1,000 credits. One credit equals one conversation evaluation or agent test run. There is no seat fee and no minimum contract. New workspaces get 50 free credits on signup — no credit card required. Credits do not expire.
Yes. Conversation data is processed in isolated tenant environments and is never used to train shared models. For regulated industries, additional data residency and processing controls are available.
When Tovix finds a failure in production - say, an agent that misunderstands upgrade intent - it creates a reusable test scenario from that failure. The next time you change your agent, that test runs automatically. Failures caught in production become regression prevention for future launches. One real failure, caught once, can protect every release going forward.
Typically three teams: Product teams use it to understand which agent behaviors need improvement and to verify fixes before shipping. Customer Success and Operations teams use it to identify the most common unresolved interactions. Risk and compliance teams use it to review high-risk conversations and document decisions. Tovix is cross-functional by design.
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