6-Phase Engagement
From first conversation
to systems in production
Each phase is designed to de-risk the next, building architectural clarity, working software, and operational confidence in sequence.
Step 01 · Align
Understand Goals, Constraints, and Non-Negotiables
Architecture decisions made before understanding constraints produce systems that have to be rebuilt
Before a single architecture diagram is drawn, the full picture is established: business outcomes required, existing integrations, security and compliance boundaries, team capabilities, and budget realities. This alignment session prevents the most common and expensive failure mode in AI engineering.
Shared architectural principles before any code is written
Step 02 · Architect
Design the Right Agent Topology for Each Scenario
One architecture does not fit all agentic scenarios. Using pro-code where low-code suffices, or vice versa, creates unnecessary cost and complexity
The agent topology is designed per use case: which workloads belong in Copilot Studio, which require Foundry Agent Service, and which need the full Agent Framework with Durable Functions. MCP server integration points, knowledge sources, tool definitions, and orchestration patterns are all specified before implementation begins.
A clear, justified architecture your team understands and can own
Step 03 · Validate
Prove Feasibility Before Full Commitment
Skipping the PoC phase and going straight to full build is the single most common cause of failed AI projects
A focused proof-of-concept sprint validates the critical unknowns: data access, model performance, latency, integration complexity, and governance requirements. Hard problems surface early, when they are cheap to solve, before full engineering investment locks in the approach.
Architectural confidence grounded in working code, not assumptions
Step 04 · Build
Engineer the Production System
Agent systems require disciplined engineering. State management, error handling, and evaluation are not optional
Full implementation across the chosen stack: Foundry Agent Service for hosted agents with knowledge integration and tool execution; Agent Framework for complex orchestration, durable stateful workflows, and SKILL.md-defined capabilities; M365 Agents SDK for Copilot Chat, Teams, and AG-UI front-end integration. Python and C# throughout.
Production-ready agents with full state management, tools, and observability
Step 05 · Harden
Test, Evaluate, and Secure Before Go-Live
Agents that aren't properly evaluated will hallucinate, over-permission, or produce outputs that erode user trust irreversibly
Systematic evaluation using Foundry's evaluation framework covers factual accuracy, relevance, safety, and groundedness. Security review against Azure AI security best practices. Responsible AI guardrails via content filters, input/output validation, and human-in-the-loop checkpoints. Compliance readiness verified against your governance requirements.
Go-live confidence: agents that are accurate, safe, and compliant
Step 06 · Operate
Observability, Governance, and Continuous Improvement
AI systems degrade silently. Without ongoing monitoring and governance, agents become less accurate and more risky over time
The operational model covers tracing and observability via Azure Monitor and Application Insights, governance controls through Microsoft Purview and Copilot Controls, and a continuous evaluation cadence. Full handover includes runbooks, architecture documentation, and connection to specialist training classes for internal capability building.
An operational system your team can run, extend, and improve independently
Ready to build something
that actually ships?
Book an architecture review call. We'll look at your specific use case and define the right technical approach — no generic prescriptions.
Frequently Asked Questions
When should I choose Microsoft Foundry over Copilot Studio for AI development?
Choose Microsoft Foundry when you need enterprise-grade multi-agent orchestration, custom evaluation pipelines, full governance, or tight integration with Azure infrastructure. Copilot Studio is the right choice when business teams need to build department-level agents without engineering involvement, or when the use case fits within the M365 ecosystem. Most organizations run both: Copilot Studio for business-owned agents, Foundry for engineering-built systems.
What does a typical AI architecture engagement deliver?
A typical engagement delivers an architectural design document, a working proof-of-concept or production-ready system, infrastructure as code (Bicep), CI/CD pipelines on GitHub Actions, evaluation harnesses for ongoing quality monitoring, and documentation sufficient for your team to operate and extend the system independently.
How do you ensure security and compliance in AI systems?
Security and compliance are addressed at the architectural level from the start. We use Microsoft Purview for data classification and access policy enforcement across all agent interactions, Azure RBAC for fine-grained permission management, private networking where required, and audit logging for every agent action. Responsible AI guidelines and content filters are configured before any system goes to production.