How I Accelerated Dynamics 365 Delivery with AI Agents

Every greenfield Dynamics 365 project begins under the same conditions: a fixed deadline, no legacy system to reference, and a long list of deliverables that must exist before configuration can start. Requirements, architecture, technical design, a sprint plan, a test strategy and a deployment runbook each demand senior time, and on several of our projects that work took longer than the building itself.

I set out to address that problem. The result, LTM Dynamiq, grew into something broader: an agentic accelerator that carries a project from the first workshop notes through Dataverse provisioning, UI configuration, test design and deployment planning, with a human approval gate at every step.

The Problem Is Consistency, Not Effort

Delivery teams rarely lack effort. What they lack is consistency. Document quality varies with whoever is writing that week. Standards tend to live in the heads of one or two architects. Reviews catch only what a given reviewer happens to notice. Most damaging of all, artefacts drift apart: the BRD, the backlog, the data model and the test cases are written at different times by different people, and the gaps surface late, usually in UAT.

On a greenfield project, with nothing to compare against, those gaps are harder to spot and more expensive to close.

What LTM Dynamiq Does

LTM Dynamiq is a team of more than 25 specialist agents, each modelled on a role in a real delivery team: business analyst, solution architect, technical architect, delivery manager, functional consultant, QA lead and DevOps lead. Six reviewer agents and an orchestrator sit alongside them. Work moves through eight phases, and each phase produces working artefacts rather than notes: Word documents, backlog items and machine-readable specifications that the next phase consumes.

The early phases establish the design: a consolidated brief from client material, a MoSCoW-prioritised BRD with process flows, the solution and ALM architecture, and a technical design covering the data model, security roles and integrations. The later phases are where the accelerator moves beyond documentation.

From Design to Build

Three integrations turn approved designs into working assets.

• Azure DevOps backlog. The approved BRD is decomposed into epics, features, stories and tasks and created directly in Azure DevOps. Work item IDs are written back, giving traceability from requirement to delivery.
• Dataverse provisioning. The approved data model is applied to the environment through the Dataverse Web API: publisher, solution, tables, columns, global option sets and relationships. Runs are idempotent, and a dry-run mode shows every change before it is applied.
• UI configuration with Playwright. Much of Dynamics 365 configuration is still done through the maker experience. Playwright drives it directly to configure forms, views, the sitemap, business process flows and ribbon elements.

The deployment phase completes the chain with a CI/CD pipeline definition in YAML, a cutover plan, rollback criteria with measurable triggers, and a two-week hyper care plan.

Testing Built In From the Start

Testing is usually the first activity to be compressed when a schedule slips. In LTM Dynamiq it is derived directly from the requirements. The QA agent produces a test pack spanning unit, SIT, UAT, performance, security and regression testing.

Because Playwright both configures the UI and drives it, the screens being built are the same screens being tested. That closes the gap I have seen on almost every project: tests written against an assumed design rather than the configured one.

Governance by Design

An AI system that produces delivery artefacts must earn trust before it is relied on. Three rules are therefore non-negotiable: all artefacts remain within the project's own systems, every phase closes with approval from a named individual, and every document carries a history of who drafted, reviewed and changed it. These controls add little overhead, and they ensure that nothing progresses without accountable sign-off.

The platform is also designed to fit each client. It can load a client's document template, naming conventions and security policy, draw on Dynamics 365 standards and Microsoft Learn guidance, and includes skill packs for Customer Service, Sales, Marketing, Field Service, Copilot Studio and canvas apps. Project managers can operate it through a Power Apps front end without touching the underlying pipeline.

The Business Case

A single run produces a complete first pass across documentation, backlog, Dataverse schema, UI configuration and test assets, work that would typically occupy a small team for several weeks. The time saved varies with scope, so I avoid quoting a single figure. The more durable benefit is consistency: every artefact meets the same quality bar before a person sees it.

That changes the nature of review. Sessions shift from finding missing content to confirming decisions, and senior architects spend their time on the judgements that genuinely need them.

Lessons Learned

• Resilience matters early. Relying on one model per step meant a single outage could halt a run. A fallback model list resolved this.
• Scores must be diagnostic. A single pass/fail result hid where an artefact was weak. Scoring across ten dimensions made feedback actionable.
• Review needs depth. One review cycle missed material feedback. Allowing up to three cycles improved outcomes noticeably.
• Reviewer suggestions must be acted on. When a reviewer proposed a section that did not yet exist, early versions ignored it. The system now creates the section.

The limits are equally clear. Output quality depends on the standards the system is given. Human expertise is required at every gate. And the accelerator currently supports greenfield implementations only, not upgrades or migrations. It does not replace architects, consultants or testers; it gives them a faster and more consistent starting point.