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Cetina
Rodriguez
Choosing a technology is only part of the decision. You also need a way of working that matches the outcome you want, how often priorities change, and what your team needs after launch.
Ventagium offers three engagement models: fixed-scope projects, teams of Forward Deployed Engineers, and operational support. All three apply to initiatives built with Microsoft Fabric, Power Platform, and AI agents, including solutions that combine them. None is universally better. Each addresses a different delivery need.
Fabric can provide the data and analytics foundation, Power Platform can support business applications and workflows, and AI agents can help people retrieve information or complete approved tasks. The engagement model defines how we organize delivery and responsibilities, not which technology you must use.
A fixed-scope project works best when we can agree on what needs to be delivered and how success will be evaluated. The scope may involve one technology or several working together.
Examples include a Microsoft Fabric reporting solution, a Power Apps application for managing requests, a Power Automate approval workflow, or an AI agent that answers questions from an approved knowledge base and hands off requests it cannot resolve.
A scoping workshop brings business and technical stakeholders together to define the problem, users, current processes, desired outcomes, dependencies, and responsibilities. We review relevant data sources, integrations, access requirements, and governance constraints.
For AI-agent initiatives, discovery also identifies what the agent may access, which actions it may take, when human approval is required, and how its behavior will be evaluated. If feasibility is uncertain, a bounded proof of concept may be the next deliverable rather than a commitment to a full production rollout.
Acceptance criteria should reflect the solution. A Fabric implementation might be evaluated against data reconciliation, freshness, and reporting requirements. A Power Platform solution might be checked against workflow rules, permissions, exception handling, and user acceptance scenarios.
For an AI agent, acceptance should use agreed test scenarios and quality thresholds, along with checks for access controls, permitted actions, and escalation behavior. It should not assume that every generated response will be correct.
The delivery plan defines milestones, testing, deployment, documentation, and handover. Changes to agreed requirements receive a scope and schedule review before proceeding. Data access, stakeholder availability, and timely decisions remain important customer dependencies.
Choose this model when: you need a specific outcome, can establish acceptance criteria, and want clear delivery boundaries.
Some initiatives develop as your organization learns. A reporting project may uncover a workflow bottleneck. A new business app may create a need for better analytics. An AI-agent pilot may reveal additional integrations or evaluation requirements.
A team of Forward Deployed Engineers (FDEs) provides sustained delivery capacity for that evolving roadmap. Ventagium embeds professionals who work alongside your organization, with roles and expertise aligned to the work. The team can focus on one technology or combine data engineering, application development, automation, and AI expertise as needed.
Work is organized through weekly sprints and quarterly on-site planning sessions to maintain tactical and strategic alignment. A typical team includes three professionals, with composition adapted to the initiative.
For example, a team might first establish trusted data in Fabric, then build a Power Apps interface for an operational process, automate approvals with Power Automate, and introduce an AI agent for a well-defined task. This is an illustrative roadmap, not a requirement to use every technology.
Priorities and tradeoffs are reviewed together. The engagement provides team capacity, not unlimited deliverables. An evolving roadmap still requires clear ownership, acceptance criteria, testing, and release decisions. Agent permissions and human oversight should be reviewed when new capabilities are introduced.
Choose this model when: you have several use cases, changing priorities, or a broader transformation initiative that benefits from sustained delivery and continuity.
Launching a solution does not end the work. Data sources change, connectors fail, business rules evolve, and users request improvements. AI agents also need their behavior reviewed as knowledge sources, models, and connected tools change.
Operational support combines monitoring and maintenance with capacity for technical debt, bugs, and agreed small enhancements. Coverage is defined around the solution and the responsibilities agreed with your team.
For Microsoft Fabric, support can include pipeline health, data freshness, failed refreshes, performance, and reporting issues.
For Power Platform, it can include app errors, failed flows, connector issues, permissions, and agreed environment and deployment maintenance.
For AI agents, it can include availability, tool failures, response times, agreed quality evaluations, and escalation signals. Prompt, knowledge, model, or tool changes should be tested before release. Where agents can take actions, support should include the agreed monitoring of permission and approval boundaries.
Findings feed a prioritized improvement backlog. Small enhancements might include refining a report measure, updating a validation rule, adjusting an existing flow, or improving an agent's instructions within its approved purpose. Work is implemented, tested, and verified before closure.
The engagement defines delivery capacity, response hours, escalation procedures, and what qualifies as a small enhancement. Automated monitoring does not automatically include round-the-clock human response.
A new application, major integration, or expansion of an agent's permissions receives a separate scope review. An existing solution can enter support after an agreed readiness review and handover, even when Ventagium did not build it.
Choose this model when: your solution is already live and needs dependable care and incremental improvement.
The same technology can fit any of these models. A Power Apps solution can be a fixed-scope project, part of an FDE roadmap, or covered by operational support. The same is true for Fabric and AI agents. Choose according to scope clarity, the pace of change, internal team capacity, and the responsibilities you need Ventagium to own.
If the problem or feasibility is unclear, start with discovery. Uncertainty alone is not a reason to commit to an ongoing team.
A fixed-scope project may transition into operational support after acceptance and handover. An FDE team may deliver new capabilities while operational support maintains existing workloads. Define ownership of releases, incidents, and shared components so responsibilities do not overlap or leave gaps.
For example, support might maintain an existing Fabric reporting solution while an FDE team develops a Power Platform application and an AI agent connected to the same data. The technology boundaries and engagement boundaries do not have to be identical, but ownership must remain clear.
Bring your priority use cases, current systems, internal team capacity, and desired outcomes. You do not need to have selected a technology or engagement model before starting the conversation.
Request a conversation with Ventagium to discuss whether a scoping workshop, fixed-scope project, embedded team, or operational support fits your next step.
For Fabric-specific pricing guidance, see Microsoft Fabric Costs: Pricing & Implementation - Ventagium. That article focuses on Fabric and should not be treated as a pricing guide for Power Platform or AI-agent engagements.
