# Application Lifecycle Management ## Power Platform ALM. Done right. At scale. Recognized by Microsoft as “the best there is” in Power Platform ALM. We design and implement production-grade pipelines for organizations that have outgrown spreadsheets, manual exports, and one-solution-at-a-time thinking. ### Why It Matters ## Agents are building apps now. Your processes need to keep up. AI agents are writing code, generating components, and assembling solutions faster than ever. But speed without process is just faster chaos. ALM gives both humans and agents the guardrails, validation gates, and deployment discipline to ship with confidence. ### Agents Need Workflows Too AI-generated components still need to be validated, tested, and promoted through environments. Without ALM, agent output bypasses every quality gate you have. ### More Output, More Risk The faster you produce, the faster you can break things. Structured deployments with approval gates protect production from the velocity that agents create. ### Auditability Is Non-Negotiable When a human and three agents all contribute to a release, you need to know exactly what changed, who approved it, and how to roll it back. ## The Reality ## Power Platform doesn't play by standard DevOps rules. Standard CI/CD assumptions break on Dataverse. Git can't merge solution XML. Plugin uploads silently overwrite each other. Configuration is environment-specific but embedded in the package. Most teams learn this the hard way. We already have. ### Dataverse is the merge engine, not Git Solution components can only be reliably merged inside Dataverse itself. Standard Git branching and merging strategies don’t apply to solution metadata. ### Multi-developer work causes silent collisions Two developers uploading plugins to the same environment? The second silently overwrites the first. Without isolation strategies, work is regularly lost. ### Configuration varies per environment Connection references and environment variables must resolve differently in dev, test, and production — but they live inside the solution package. ### Deployment sequencing is unforgiving Connection references fail during import if not configured. Reference data can only load after tables exist. A single deployment step is never enough. ## Our Approach ## A dual-loop deployment model built for how the platform actually works. We separate development-time iteration from release-time promotion into two distinct mechanisms, each with its own tooling, validation, and purpose. ### Inner Loop - Daily Development 1. Develop Developer iterates in feature solution 2. Feature Branch Sync, export, and unpack to Git 3. Dev-Test Build, compile, and import as managed 4. Dev-Integration Transport components to integration solution ### Outer Loop - Release & Promotion 1. develop → main PR triggers release build 2. Build Compile, version, package all solutions 3. GitHub / Azure DevOps Release Versioned artifacts with deployment settings 4. Test → UAT → Production Approval-gated promotion to each environment ### Release Promotion Path DevIntegrationTestUATProduction Each promotion requires approval gates. Deployments are sequential to prevent dependency conflicts. ## Agents in the Loop ## Your developers say what to do. The agent runs the pipeline. We build **agent-directed workflow skills**: Markdown files checked into your repo that encode the full operational knowledge for every ALM task. Checklists, parameter gathering, decision trees, validation steps. All with guardrails. A developer types a natural language prompt in VS Code Copilot Chat. The matching skill activates, gathers missing inputs, selects the right script, and executes. No docs lookup, no context switching, no mistakes. ### Copilot Chat 👤 start feature AB12345 add approval workflow 🤖 Created branch + feature solution in development. Set as preferred solution. 👤 sync solution from development 🤖 Exported, unpacked to feature branch. 12 files changed. 👤 build and deploy to dev-test 🤖 Built plugins + controls. Deployed managed to dev-test. Settings applied. 👤 transport AB12345 to integration 🤖 Validation passed. 14 components transported. PR opened for code-first changes. ## Capabilities ## Enterprise ALM. Purpose-built for Power Platform. We work with GitHub Actions, Azure DevOps, and GitLab CI. Every implementation is designed around how Dataverse actually works, not how generic CI/CD tools assume it works. ### Zero-Secrets Auth (OIDC) Federated credentials issue short-lived tokens at pipeline runtime. No stored client secrets, no manual rotation, no leaks. ### Smart Change Detection Monorepo builds detect exactly which solutions, plugins, and controls changed — including transitive dependencies — and build only what’s needed. ### Feature Isolation Developers work in isolated feature solutions. Transport workflows merge only relevant components into integration, preventing collisions. ### Settings Pipeline Connection references and environment variables are templated, merged at build time, and validated before deployment — not patched afterward. ### Extensible Hook System Convention-based pipeline hooks let you plug in post-deploy flows, data imports, and custom validation without modifying core scripts. ### Agent-Directed ALM Skills Markdown-based workflow skills let AI agents run the full ALM lifecycle — sync, build, deploy, transport — with guardrails, validation, and zero docs lookup. ### Versioned Release Packages Date-based versioning, multi-solution package bundles, and per-environment deployment settings — one command deploys to any target. ## How We Work With You ## From assessment to production. ### 01 - Planning ### Assess & Architect ALM is not one-size-fits-all. We evaluate your current state: solutions, environments, team structure, and release cadence. Then we design a branching, deployment, and environment strategy that matches your organization. ### 02 - Implementation ### Build & Automate We build the pipelines, write the scripts, configure OIDC auth, set up environment approval gates, and wire up the full inner and outer deployment loops. You get a working system, not a slide deck. ### 03 - Customization ### Extend & Adapt Need custom validation hooks, automated data imports, or plugin registration without the GUI tool? We extend the pipeline with solution-specific logic while keeping the core scripts untouched. ### 04 - Support ### Operate & Evolve We don't leave when the pipeline is green. As your platform grows with new solutions, new environments, and new team members, we help you evolve the system to match. ## Case Studies ## Real implementations. Production results. ### Multi-Solution Platform #### Production-Grade ALM for a Multi-Solution Dataverse Platform Designed and built a fully automated CI/CD system for four interconnected Dataverse solutions across nine environments using GitHub Actions, OIDC federated auth, and a dual inner/outer deployment loop. - **4 Solutions** - **9 Environments** - **13 Workflows** ### Enterprise Migration #### Enterprise ALM Overhaul for a Large-Scale Dynamics 365 Platform Migrated a six-year, 40 GB Azure DevOps repository to GitHub, replaced ad hoc pipelines with a purpose-built GitHub Actions architecture, and implemented zero-secrets OIDC auth across eight environments. - **30+ Solutions** - **8 Environments** - **25 MB from 40 GB** ## Ready to get serious about ALM? Whether you're starting from scratch or migrating a legacy pipeline, we'll design an ALM system that works with the platform, not against it.