mablが2026年のGartner Magic Quadrantでチャレンジャーに位置付けられ、AI駆動テストの最新機能を実装！

mabl is the agentic testing harness that helps quality keep pace with AI-powered development.

Boston was always going to find a way to give artificial intelligence extra homework. You cannot build an entire regional personality around office hours, technical diligence, and asking one more question after the panel has ended, then watch a robot submit code with “looks good to me” as its supporting evidence. This week, mabl announced new verification capabilities for software produced by coding agents. The release includes agent objectives and guardrails, checks accessible from the developer’s terminal, coordination across testing workspaces, and integrations with test-management systems including Xray, Zephyr, TestRail, and Azure DevOps. Co-founder Dan Belcher’s pitch is that teams should establish the standards their agents must satisfy before changes reach a pull request. This is a useful incremental move with a consequential premise: making software faster only helps if someone can determine whether the resulting software works. Mabl is selling the examination apparatus for a world suddenly rich in extremely confident students. ## Downtown Crossing, Department of Showing Your Work The local connection is substantive. Mabl’s company profile identifies Boston as its headquarters and lists its primary office on Chauncy Street. This is local enterprise software, rather than a visiting executive discovering Cambridge while waiting for a car to Logan. It fits an appealing strand of the Massachusetts ecosystem: tools that make other ambitious tools usable. Our recent coverage of Tamr’s work cleaning up enterprise data concerns a related problem. Before an agent acts confidently, somebody needs to establish that the information and machinery underneath it deserve that confidence. Readers outside Massachusetts have an obvious stake. A checkout page does not care where its testing vendor rents office space. It cares whether a discount changed the total correctly, whether payment completed, and whether the confirmation screen describes the order actually placed. These are wonderfully concrete questions in an industry that keeps trying to replace questions with the word “agentic.” ## The Test Has to Find the Homework First Mabl’s test impact analysis documentation explains the mechanism. The system translates code changes into descriptions of affected product behavior, then searches test names, descriptions, and steps by wording and meaning. A purchase-flow change can therefore connect to a test described as checkout. Where available, a knowledge graph maps application features and workflows to their tests. There is an important availability wrinkle: that documentation labels knowledge graphs early access, with rollout over coming months and pricing and packaging subject to change. The announcement also lists desktop application testing as coming soon. Those details belong in the purchasing conversation before anyone books a celebratory offsite. The same technical page says results are not exhaustive, API and performance tests may be missing, and two analyses can return different lists. Finding no gaps does not establish complete coverage. That is unusually helpful candor. A test-selection system is making an informed recommendation, not issuing a mathematical certificate that your software has achieved inner peace. Imagine changing a shared shopping-cart component. A useful assistant would find the relevant purchase tests, explain its selections, and identify missing scenarios. A dangerous interpretation would be to assume every unmentioned tax, accessibility, or payment behavior must therefore be fine. The difference is human judgment, supported by evidence rather than displaced by a green badge. ## A Passing Grade Needs an Address The documented workflow starts after unit tests pass and before a pull request opens. It establishes the application, environment, credentials, and execution location. Read-only or self-cleaning tests can run, beginning with a canary; tests that write shared data or have uncertain cleanup need approval. Disabled, unreliable, or out-of-scope tests are skipped with reasons. The resulting report identifies the build and commit tested, what passed or failed, what did not run, and apparent gaps. That distinction is excellent. Testing yesterday’s build successfully is a very efficient way to learn something reassuring about yesterday. Our coding-agents deep dive explored the move from autocomplete to delegated work. Verification has to follow that delegation into the actual workflow. If developers must leave their tools, reconstruct context, and hand-copy results, the checking process becomes the thing everyone sincerely supports right up until the deadline. ## Independent Does Not Mean Infallible Mabl’s architecture documentation describes separate model systems for the coding assistant and mabl’s own capabilities, even when they use the same provider. Its MCP connection gives an assistant access to testing tools under the permissions of the authenticated user or scoped API key. Test data can become part of the coding assistant’s context, governed by that assistant’s model-provider terms. That makes “independent” a useful architectural distinction, not a promise of unrelated failure modes or an external certification. Two systems can share assumptions. A test can faithfully enforce the wrong requirement. The person defining what success means still has a job, and it is increasingly the interesting job. As our piece on independent AI reviews argued in a different context, the evidence and the conditions surrounding a check matter. A separate reviewer is a good beginning. Understanding what that reviewer actually examined is better. ## There Are Customers Behind the Vocabulary This is an established testing business extending its workflow, not merely a new noun looking for a subscription. In a November 2022 customer presentation, JetBlue described using mabl to automate prerequisite booking data and reduce repetitive testing work. Its team also described checking flaky tests and manually reviewing failures. That is historical evidence of practical use, not proof of adoption or performance of this week’s features. The release reports 3.3 million agent sessions over the preceding 90 days and estimates roughly 100,000 engineering hours saved. Those are company figures; activity volume and modeled time savings do not independently establish fewer production defects. Buyers should measure escaped bugs, investigation time, missed scenarios, and total operating cost on their own applications. My verdict is warmly positive. Mabl is addressing a real coordination problem with a workflow concrete enough to inspect and documentation honest enough to be useful. The commercial test is whether teams get dependable evidence with less effort, rather than simply acquiring another system whose confidence they must supervise. Boston has contributed many ambitious ideas to technology. Asking the robot to attach its working is a smaller one. It may also be among the easiest to appreciate when the checkout button still works on Friday afternoon.

mabl has unveiled a new testing harness designed to enhance software verification for engineering teams, ensuring reliability in agent-written code. This advancement promotes developer confidence and streamlines workflows.

ジャパンシステム株式会社「自治体システムの未来を支える品質基盤改革」をテーマに、公共事業本部本部長の山際賢が講演 ジャパンシステム株式会社（本社：東京都渋谷区、代表執行役社長：松野 克哉、以下当社）は、2026年11月9日(月)に開催されるmabl株式会社主催の「mabl Experience 2026 Japan」において、当社の常務執行役兼公共事業本部本部長 山際 賢が登壇することをお知らせします。当日は「自治体システムの未来を支える品質基盤改革」をテーマに、品質保証を仕組として定着させる取り組みや今後の展望について講演します。 「mabl Experience 2026 Japan」は、mabl株式会社が、ユーザーによる活用体験や学び、意見の共有を通じて、mablのベストプラクティスへの理解を深めることを目的に開催する年次イベントです。 ■イベント概要 ■セッション概要 ■講演者および講演内容について ジャパンシステム株式会社　 常務執行役兼公共事業本部本部長　 山際 賢（やまぎわ さとし） ジャパンシステム株式会社　常務執行役兼公共事業本部本部長　山際 賢（やまぎわ さとし）当社は、40年以上にわたり地方自治体向けの財務会計システムを提供し、全国430団体以上への販売実績を有するパッケージベンダーです。モダナイゼーションやクラウド化、AI活用の進展により、公共システム開発を取り巻く環境は大きく変化しています。その中で求められるのは、高い品質を維持しながら変化へ迅速に対応できる開発体制の構築です。本セッションでは、ジャパンシステムが取り組む新しい品質基盤づくりをご紹介します。mablを活用したAI×テスト自動化により、継続的な品質向上と開発リードタイム短縮の両立を図るとともに、品質保証を仕組みとして定着させる取り組みや今後の展望についてお話しします。 ■ジャパンシステム株式会社について（https://www.japan-systems.co.jp/） 本社：〒151-8404 東京都渋谷区代々木1-22-1 JRE代々木一丁目ビル 設立：2020年7月（創立 1969年6月） 代表者：代表執行役社長 松野 克哉 事業内容：コンサルティング/AI導入・アプリ開発/クラウド・BI/自社パッケージ・システム開発/インフラ構築、他 ＜講演に関するお問い合わせ＞ ジャパンシステム株式会社　公共事業本部 お問い合わせフォーム：https://public.japan-systems.co.jp/contact/企業プレスリリース詳細へPR TIMESトップへ

ジャパンシステム株式会社「自治体システムの未来を支える品質基盤改革」をテーマに、公共事業本部本部長の山際賢が講演 ジャパンシステム株式会社（本社：東京都渋谷区、代表執行役社長：松野 克哉、以下当社）は、2026年11月9日(月)に開催されるmabl株式会社主催の「mabl Experience 2026 Japan」において、当社の常務執行役兼公共事業本部本部長 山際 賢が登壇することをお知らせします。当日は「自治体システムの未来を支える品質基盤改革」をテーマに、品質保証を仕組として定着させる取り組みや今後の展望について講演します。 「mabl Experience 2026 Japan」は、mabl株式会社が、ユーザーによる活用体験や学び、意見の共有を通じて、mablのベストプラクティスへの理解を深めることを目的に開催する年次イベントです。 ■イベント概要 ■セッション概要 ■講演者および講演内容について ジャパンシステム株式会社　 常務執行役兼公共事業本部本部長　 山際 賢（やまぎわ さとし） ジャパンシステム株式会社　常務執行役兼公共事業本部本部長　山際 賢（やまぎわ さとし）当社は、40年以上にわたり地方自治体向けの財務会計システムを提供し、全国430団体以上への販売実績を有するパッケージベンダーです。モダナイゼーションやクラウド化、AI活用の進展により、公共システム開発を取り巻く環境は大きく変化しています。その中で求められるのは、高い品質を維持しながら変化へ迅速に対応できる開発体制の構築です。本セッションでは、ジャパンシステムが取り組む新しい品質基盤づくりをご紹介します。mablを活用したAI×テスト自動化により、継続的な品質向上と開発リードタイム短縮の両立を図るとともに、品質保証を仕組みとして定着させる取り組みや今後の展望についてお話しします。 ■ジャパンシステム株式会社について（https://www.japan-systems.co.jp/） 本社：〒151-8404 東京都渋谷区代々木1-22-1 JRE代々木一丁目ビル 設立：2020年7月（創立 1969年6月） 代表者：代表執行役社長 松野 克哉 事業内容：コンサルティング/AI導入・アプリ開発/クラウド・BI/自社パッケージ・システム開発/インフラ構築、他 ＜講演に関するお問い合わせ＞ ジャパンシステム株式会社　公共事業本部 お問い合わせフォーム：https://public.japan-systems.co.jp/contact/企業プレスリリース詳細へPR TIMESトップへ

mabl has been recognized as a Challenger in the 2026 Gartner Magic Quadrant for Agentic Software Quality Assurance Platforms, showcasing its focus on innovation and quality verification.

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