Why this journey matters 

My journey with Bot, virtual agents and personal assistants has been quite long and, in this time, not only has the usage and user scenario evolved but the technology and platforms that fueled it significantly changed as well. Agentic solutions are no longer just “chat with documents, knowledgebases or hand curate the decision making into the AI services” - The bar has moved to systems that understand context, invoke tools, and complete workflows—with the governance and telemetry your business requires, and the new tools that are at our disposal.  

In this article, I’m going through the notes that I have made and formulated approaches that I go through as I work on new AI solutions and AI projects. I have also added a checklist and a 90-day plan, if you are lucky enough to launch an AI Agentic project and want to start in a structured way from small wins to big bang. 
While navigating various scenarios and projects, I have developed and refined this practical approach/progression. This methodology gradually evolved as I encountered different timeline constraints and use cases. 

• No‑code for rapid wins inside Microsoft 365 

• Low‑code for richer conversation design and workflow orchestration 

• Pro‑code for robust model choice, evaluation, safety, and operations on Azure 

Use it as a blueprint to decide where to start, when to step up, and how to land production quality without over‑engineering day one. With this approach, I have seen team formation evolve as well. While some use cases will hit fruition at Low-code stage itself, there will be few that will be adopted for Pro-code and involve larger Development team and more matured, DevOps processes.  

 

 The spectrum at a glance 

Start: No‑code with Microsoft 365 Copilot Agent Builder 

When you need impact now, or something that you want to automate quickly, including your daily routine or a quick business process - embedded intelligence where people work every day. 

What you can achieve 

• Answer policy and product questions grounded in your internal content 

• Automate simple tasks (drafts, reminders, status messages) 

• Share quickly in Teams to capture user feedback 

• Collaborate and share with your teammates. 

How to approach 

1. Define one job to be done (e.g., “answer 80% of field FAQs”). 
2. Attach one high‑quality content source (structured SharePoint library beats scattered files). 
3. Add one action that saves clicks (create a task, send a summary). 
4. Pilot with a small group; measure deflection, satisfaction, and turnaround time. 

Guardrails from day one 

• Keep scope narrow, content curated, and responses concise. 

• Document the agent’s mandate and what it won’t do (set expectations). 

 Level up: Low‑code with Copilot Studio 

Transition to this approach when your project requires designed conversations, conditional logic, and system actions—all without needing to move into full pro-code development. This method is especially effective for quickly deploying agents across a department, particularly for straightforward use cases, simple automations, and workflows that require more extensive reach. It enables broader automation and process improvement while maintaining a low-code approach that remains accessible to a wider range of users.  

What you can achieve 

• Model topics/intents and multi‑turn dialogues. 

• Call internal and external APIs via custom connectors 

• Apply business rules before actions are carried out. 

Design tips 

• Structure the conversation: greet → clarify → retrieve/act → confirm → summarize. 

• Separate knowledge from behavior: keep content where it’s governed; keep logic in Studio. 

• Instrument outcomes: track successful task completion, not just messages exchanged. Deep analytics into usage etc. 

Integration patterns 

• Internal systems (HR, finance, CRM) through connectors. 

• Event-driven flows (create tickets, update records, trigger notifications). 

• Approval handoffs when confidence is low. 

Production grade: Pro‑code with Azure AI Foundry 

When correctness, safety, scale, and cost matter, graduate to developer tooling on Azure. 

Why this layer 

• Model choice: right‑fit models (capability, latency, cost) for each task. 

• Prompt orchestration: multi‑step reasoning and tool calling. 

• Evaluation: offline tests before release and live monitoring after. 

• Safety: input/output filtering and policy enforcement. 

• Operations: CI/CD, observability, and performance management.
• Standard development process and tooling: I emphasize largely AI Models and Azure AI Foundry here, however the standard development practices, code security, Identity and access, compliance, testing etc. will remain same.  

Engineering flow that works 

1. Frame the objective: Define success metrics (quality, safety, and business KPIs). 
2. Prototype prompt flows: Start small, version them, and add tool calls only where needed. 
3. Evaluate before you ship: Use curated datasets for offline tests; include tricky edge cases. 
4. Harden safety: Enable content filters, set thresholds, and log decisions for auditability. 
5. Ship with telemetry: Track latency, cost per task, answer accuracy, and user feedback. 
6. Continuously improve: Roll updates behind flags, watch for drift, and retrain or return when needed. 

Reference architecture (conceptual) 

Experience → Teams/web/app 

Orchestration → Copilot Studio (dialog, routing, actions) 

AI Services → Azure AI Foundry (models, prompt flows, evaluation, safety, monitoring) 

Enterprise systems → Data platforms, line‑of‑business APIs, automation services 

Key principles 

• Separation of concerns: UI ≠ Conversation logic ≠ Model/runtime ≠ Business systems. 

• Least privilege: Only the permissions and scopes the agent truly needs. 

• Observability first: Logs, traces, and quality events from day one. 

• Human‑in‑the‑loop: Escalation paths for low‑confidence or sensitive requests. 

My 90‑day plan 

Days 1–30: Prove value 

• Ship two no‑code agents for different teams. 

• Measure deflection %, response helpfulness, and time saved. 

Days 31–60: Orchestrate actions 

• Rebuild one agent in Copilot Studio with a clear dialog flow. 

• Add a secure API action and an approval fallback. 

Days 61–90: Operationalize 

• Port the highest‑impact scenario to Foundry. 

• Implement offline evaluation, enable safety filters, deploy to a controlled audience, and set up monitoring dashboards. 

 

Design checklists (save for later) 

No-code launch checklist  

Low-code orchestration checklist  

Pro-code readiness checklist  

 

Common pitfalls and how to avoid them 

• Starting big: Begin with one clearly defined outcome; expand only after you see measurable impact. 

• Over‑indexing on chat: Instrument task completion, not just message counts. 

• Hidden coupling: Don’t bury business logic inside prompts; keep rules visible and testable. 

• Skipping eval: Always gate releases with a small, representative test set. 

• No feedback loop: Capture user feedback in‑product and close the loop with updates. 

Final take 

Stay on the course and go progressive: 

1) No‑code for momentum and adoption, 

2) Low‑code for richer conversations and actions, and 

3) Pro‑code for the rigor that production demands. 

Treat evaluation, safety, and observability as core features and focus on it from day 1, not afterthoughts. That’s how you build agentic solutions that are useful on day one and trustworthy on day one hundred. 

These links cover the full journey from no-code to pro-code, including responsible AI practices: 

Microsoft 365 Copilot Agent Builder Overview 

•  https://learn.microsoft.com/en-us/microsoft-365-copilot/extensibility/agents-overview 

Microsoft Copilot Studio Documentation 

• https://learn.microsoft.com/en-us/microsoft-copilot-studio/ 

Azure AI Foundry Documentation 

• https://learn.microsoft.com/en-us/azure/ai-foundry/ 

Responsible AI and Content Safety in Azure 

• https://learn.microsoft.com/en-us/azure/ai-services/content-safety/ 

Introduction to Microsoft AI Agent Solutions (Microsoft Learn module) 

• https://learn.microsoft.com/en-us/training/modules/introduction-microsoft-ai-agent-solutions/ 

Software Development best practices & using AI in software development 

• AI in Software Development | Microsoft Copilot 

• Architecture strategies for formalizing software development management practices - Microsoft Azure Well-Architected Framework | Microsoft Learn 

 

About the Author 
Dipanjan Ghosh is a seasoned technology leader at Microsoft with extensive experience in AI solutions, enterprise architecture, and modern developer practices. He enables organizations to adopt Microsoft AI platforms such as Copilot, Copilot Studio, and Azure AI Foundry, ensuring scalability, security, and operational excellence. With a strong foundation in cloud architecture and automation, Dipanjan bridges innovation with practical implementation. Passionate about evangelizing technology innovations, he simplifies complex concepts and inspires businesses to embrace responsible, cutting-edge solutions. #SkilledByMTT, #MSLearn, #MTTBloggingGroup