> ## Content Index > Fetch the complete content index at: https://intelligence.artificialrealitymedia.com/llms.txt > Use this file to discover other available public pages before exploring further. # AI agent testing tools all assume you have a developer - URL: https://intelligence.artificialrealitymedia.com/ai-agent-testing-tools-assume-a-developer/ - Published: 2026-09-02T12:11:00.000Z - Updated: 2026-09-02T12:11:00.000Z - Description: Coval, Hamming AI, Cekura, Maxim AI and LangWatch are built for engineers. The operator who needed testing most improvised it with a prompt. - Author: Galina Fendikevich - Tags: Health & wellness, #For AI Companies, #What needs to be built, #Import 2026-09-03 05:20 Every funded AI agent testing product is built for developers, and the person in Artificial Reality's Season 1 who most needed testing was not one. [Coval](https://coval.dev/?ref=intelligence.artificialrealitymedia.com), [Hamming](https://hamming.ai/?ref=intelligence.artificialrealitymedia.com) AI, [Cekura](https://cekura.ai/?ref=intelligence.artificialrealitymedia.com), [Maxim](https://getmaxim.ai/?ref=intelligence.artificialrealitymedia.com) AI and [LangWatch](https://langwatch.ai/?ref=intelligence.artificialrealitymedia.com) all simulate users and surface failures before deployment. Dr. Nikki Siso, a holistic health practitioner running fifteen agents in production, reached the same method from first principles with a prompt. ## What is AI agent testing software? **AI agent testing software simulates users interacting with a deployed AI system, runs them through complete conversational flows, and reports where the system failed, producing before release the failures that would otherwise be found by customers.** The distinction from traditional software testing is that outputs are not deterministic. You cannot assert that a given input produces a given string. These tools evaluate whether a conversation achieved its purpose, stayed in character and handled the path correctly, which is a different discipline from unit testing and why it became its own category. ## Who is funding this, and what does each one do? Five companies, identified by Artificial Reality as apparently relevant to the requirements this operator described. None was named or endorsed by her. - **Coval** — simulation and evaluation for AI voice and chat agents. Series A, $28M, Norwest, 2026\. The best-capitalized entrant, and simulation is precisely the mechanism Siso improvised. - **Hamming AI** — automated testing for AI voice agents. Seed, Y Combinator. Voice-oriented, which is relevant to her roadmap: she wants the assessment delivered in her own cloned voice once latency improves. - **Cekura** — testing and observability for voice and chat AI agents. Seed, Y Combinator F24\. Observability alongside testing addresses the failure she actually had, where the system froze in production and nobody knew why until it was investigated. - **Maxim AI** — evaluation and observability for AI applications. Seed, $3M, Elevation. Broader than conversation testing, covering output quality, which is where tone drift would surface. - **LangWatch** — monitoring and evaluation for LLM applications. Pre-seed or seed, approximately €1M. Continuous monitoring rather than pre-release testing. ## What did the operator's own method already cover? Most of it, at no cost. She told [Claude](https://claude.ai/?ref=intelligence.artificialrealitymedia.com) to pretend to be five very difficult clients giving half answers, ran the complete flow, and found the bugs in her fifteen-skill assessment herself instead of routing each one to her developer. That is the same core mechanism the five companies above sell, with less rigour and no repeatability. Which is the uncomfortable part of this market and also the clarifying one. If the simulation itself is improvisable in an afternoon by a person with no technical training, the simulation is not the defensible product. Repeatability is: regression coverage, and knowing that a change to skill nine did not break the path through skill three. That is genuinely different from what a person can improvise, and it is the case worth making on the pricing page. ## What is nobody testing? Four gaps, identified by Artificial Reality from what actually broke in this build rather than from vendor documentation. - **Multi-agent handoffs.** Her unfixable failures were between agents, not inside them: "I couldn't fix if the conductor and the health assessor weren't talking together." Ask any vendor to demonstrate a conductor routing to an assessor and back, not a single-agent conversation. - **Long sessions.** A forty-five-minute flow has different failure modes than a short exchange, and hers failed at the final step, after the client had already spent the forty-five minutes. - **Tone drift.** For a coaching or advisory product, output that becomes cold by the end is a failure even if the run completes. - **Continuous re-running.** One pass finds the first round of bugs. Nothing finds the path nobody re-walks after a change. ## Who is the unserved buyer? The domain expert who built the system and cannot operate the tooling. Siso built fifteen skills, found her own bugs, and remained unable to fix the layer where the worst failures lived. She names the gap directly: > "That's the one thing that's also missing — testing itself." A testing tool a non-developer can configure, read and act on would be a different product from any of the five above, and the population of non-technical people running multi-agent systems in production is growing considerably faster than the population of engineers building them. ## What should a builder change this quarter? Two things. Pick a failure mode and put it on the box, because a general evaluation suite sold as agent testing will miss the handoffs and the long-session ending that this operator actually hit. Then decide whether your buyer is the engineer or the person who wrote the content. There is one more failure mode worth designing around, because it generalizes past testing. Siso's failures were not wrong answers. They were a system that stopped, silently, at the end, after someone had spent forty-five minutes on it. In conversational products the catastrophic failure is frequently abandonment rather than incorrectness, and it is the one least likely to be caught by an evaluation suite built around output quality. ## Where this comes from S1E7: How a non-tech founder built a 15-agent AI business on Claude — the full interview with Dr. Nikki Siso. Listen or watch: [YouTube](https://www.youtube.com/watch?v=R%5FzWAgAEIMw&ref=intelligence.artificialrealitymedia.com), [Spotify](https://open.spotify.com/episode/08s9u7LSBTtGGx94b4z2MZ?ref=intelligence.artificialrealitymedia.com) or [Apple Podcasts](https://podcasts.apple.com/us/podcast/how-a-non-tech-founder-built-a-15-agent-ai/id6780699025?i=1000777283240&ref=intelligence.artificialrealitymedia.com).