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, we establish the full picture: 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
Architecture Alignment
Constraints Mapping
Stakeholder Review
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 cost and complexity
We design the agent topology 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
Agent Topology Design
MCP Integration
Orchestration Patterns
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. We surface hard problems early, when they are cheap to solve, before full engineering investment locks in the approach.
Architectural confidence grounded in working code, not assumptions
PoC Engineering
Feasibility Testing
Risk Reduction
Engineering 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
Foundry Agent Service
Agent Framework
Durable Functions
MCP Servers
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 — 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
Foundry Evaluations
Responsible AI
Security Review
Guardrails
Observability, Governance, and Continuous Improvement
AI systems degrade silently — without ongoing monitoring and governance, agents become less accurate and more risky over time
We establish the operational model: 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 — where appropriate — connection to our training classes for internal capability building.
An operational system your team can run, extend, and improve independently
Azure Monitor
Microsoft Purview
Observability
Handover
