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Home » D365 Quality Assurance in the Agentic Era
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D365 Quality Assurance in the Agentic Era

Cam SessingerBy Cam SessingerAugust 17, 2026Updated:September 18, 20262 Mins Read
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Title: D365 Quality Assurance in the Agentic Era

Speakers:

  • Paul Langowski, Managing Director of Enterprise Business Applications, BDO
  • Swamy Narayana, VP Customer Experience Programs, Dynamics 365, Microsoft
  • Magnus Perri, Group CEO, elevaite365
  • Nick Morea, VP, Global Solution Consulting, Leapwork
  • Bartosz Szpiech, Vice President, Product Development, Xplus
  • Matthew Sabath, Chief Product and Technical Officer, The TestMart

Key Takeaways:

  • Quality assurance becomes more complex in an agentic environment: As AI agents begin making decisions and taking actions within D365 workflows, testing can no longer focus solely on whether traditional software functions correctly. Partners need to account for agent behavior, outputs, and interactions with business processes.
  • Testing needs to evolve alongside AI adoption: Traditional testing approaches may not be sufficient for systems that can dynamically respond to different inputs and circumstances. QA needs to increasingly address AI-specific variables such as accuracy, reliability, consistency, and expected behavior.
  • Automation is critical to scaling D365 quality assurance: With organizations deploying more agents and increasingly complex D365 environments, automated testing can help partners validate changes more efficiently and continuously rather than relying primarily on manual testing.
  • QA should be considered throughout the implementation lifecycle — not at the end: The emergence of agentic capabilities makes it increasingly important to build testing and validation into solution design, development, deployment, and ongoing optimization rather than treating QA as a final checkpoint.
  • This creates a growing opportunity for D365 partners: As customers adopt AI agents, partners can differentiate by helping them establish reliable testing, validation, and governance practices around those agents. QA becomes part of delivering confidence in an AI-enabled D365 environment, rather than simply ensuring an implementation works at launch.

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