# Kashikoi: Simulation-based evaluation for AI agents

Canonical: https://mudpie.ai/companies/kashikoi/
Breadcrumb: [Home](https://mudpie.ai/) / [Companies](https://mudpie.ai/companies/) / [Kashikoi: Simulation-based evaluation for AI agents](https://mudpie.ai/companies/kashikoi/)
Author: Ali Abouelatta (https://mudpie.ai/authors/ali-abouelatta/)
Published: 2026-09-19
Updated: 2026-09-19
Research type: Company profile
Method: Company and accelerator sources checked 2026-09-19. Product claims are attributed to their sources; this is research, not a hands-on product trial.

Kashikoi is a simulation engine for benchmarking AI agents. It fits a team that wants to test an agent against customized, multi-turn scenarios and behavioral goals instead of trusting public benchmarks, hand-written prompts or a few happy-path traces.

## What it does

[Kashikoi’s current site](https://www.getkashikoi.com/) lets a team connect an agent, describe what it should do and run simulated interactions that produce metrics and actionable recommendations. The examples show security, data-analysis and code agents evaluated on accuracy, context understanding, severity, false positives and task-specific success.

The useful decision is whether the benchmark reflects the product’s actual values and users. Kashikoi’s [YC launch](https://www.ycombinator.com/launches/Nfa-kashikoi-simulation-engine-for-benchmarking-ai-agents) describes CPU-friendly world models that interview agents, generate diverse data and detect stale regression suites. The team says a customer can compare its agent with a competitor without writing every prompt by hand. That is an attractive promise, but the buyer still needs to inspect how scenarios are sampled, how judges are calibrated and how a score turns into an engineering change.

Kashikoi’s site displays example scores for threat detection, a data analyst and code exploration. These are product demonstrations, not independent customer results. Pricing is not public; the current path is a founder demo or waitlist-style conversation.

## Founder context and tradeoffs

The YC profile identifies Aaksha Meghawat as founder and CTO and describes simulation/evaluation work at Moveworks, transformer research at CMU and edge speech models at Apple. The launch also identifies Tim Michaud as co-founder and publishes security-research context. Their background maps to the evaluation problem, but a buyer still needs to validate its own domains and failure modes.

## Editorial take

I would shortlist Kashikoi for an agent team whose eval suite is stale or too expensive to maintain by hand. Start with one task and a known failure distribution. If the team cannot state what “good” means beyond a composite score, more simulation will create measurement noise rather than confidence.

## Quick facts

| Field | Sourced detail |
| --- | --- |
| Product | Simulation, benchmarking and behavioral evaluation for AI agents |
| Buyers | Agent developers, QA, safety and product teams |
| Public workflow | Custom world models, multi-turn interviews, metrics and recommendations |
| Pricing | Not publicly listed |
| Main gate | Scenario validity, judge calibration, regression freshness and actionability |

## Sources checked

| Source | Checked |
| --- | --- |
| [YC profile](https://www.ycombinator.com/companies/kashikoi) | 2026-09-19 |
| [Kashikoi homepage](https://www.getkashikoi.com/) | 2026-09-19 |
| [Kashikoi launch](https://www.ycombinator.com/launches/Nfa-kashikoi-simulation-engine-for-benchmarking-ai-agents) | 2026-09-19 |
| [Kashikoi research blog](https://blog.getkashikoi.com/) | 2026-09-19 |

## Cohort context

Kashikoi is listed in Spring 2025. In our 2026-09-18 directory snapshot, 97 of 143 listed companies in that cohort have YC’s primary industry label B2B (67.8%). This is a current-directory comparison, not an original intake count or a performance ranking. [Nine-cohort dataset](https://mudpie.ai/research/yc-cohorts-2026-09-19.json).

## Public website snapshot

Observed 2026-09-19T16:15:41.975Z in raw homepage HTML. This records visible metadata and advertised links, not agent execution or product quality.

| Signal | Homepage observation |
| --- | --- |
| Product description metadata | Observed |
| Canonical link | Observed |
| H1 or H2 heading | Observed |
| Typed structured data | Observed |
| Docs/developer link | Not observed in this response |
| Pricing link | Not observed in this response |
| llms.txt link | Not observed in this response |
| Markdown alternate | Not observed in this response |

[Public observations](https://mudpie.ai/research/yc-homepage-links-2026-09-19.json) · [Collection method](https://mudpie.ai/research/yc-homepage-methods/README.md). Missing links here do not establish that a capability or file is absent elsewhere.


## Author disclosure

I cofound Lazyweb and publish Mudpie. This is an owner-written publication, not an independent testing organization. Research notes distinguish observations, sourced reporting and editorial judgment.