On November 12, 2025, Seven Peaks partnered with the Electricity Generating Authority of Thailand (EGAT) for a hands-on workshop titled "Introduction to Conversational AI & Copilot Studio." Led by our engineering leadership, including Head of Engineering Jose Barbosa, Principal Solutions Architect Giorgio, and AI Engineers Thanapol and Natthaphong, the session moved participants from the theoretical foundations of Generative AI to the practical architecture of building custom agents.
Our goal was to move beyond the hype of Generative AI and into how to execute. We wanted to strip away the buzzwords and demonstrate how organizations can architect, build, and govern AI agents that solve real business problems.
What follows is the takeaways from that session: a guide on how to transition from creating simple chatbots to intelligent, orchestrated copilots. In this article, we break down the key architectural principles, practical building blocks, and governance strategies you need to turn generative AI from a novelty into a core business asset.
Before writing a single line of logic, you need to distinguish between the tools of the last decade and the ecosystem available today. Specifically, it is important to understand the architectural difference between standard chatbots and copilots.
Historically, chatbots were rigid. They relied on pre-defined conversational paths and simple Natural Language Understanding (NLU). If a user deviated from the script or used a phrase the bot wasn't trained on, the interaction failed. They were siloed, text-based, and disconnected from the broader work ecosystem.
The copilot represents a fundamental evolution. Copilots are not simple text generators. They are reasoning engines powered by Large Language Models (LLMs) and Generative AI. Unlike their predecessors, they offer a natural, multimodal interface and can understand context, intent, and complex data relationships.
This shift is powered by the Copilot Stack. At the core sits the AI orchestration layer, which manages the flow between your user interface, your data (via the Microsoft Graph), and the foundation models. This architecture allows for retrieval-augmented generation (RAG), which means instead of making things up, the AI retrieves relevant information from your knowledge sources and incorporates it into a generated response.
For enterprises, this means moving beyond simple Q&A. When you look at Microsoft 365 Copilot, you are looking at an orchestration layer that integrates with the tools, such as Teams, Outlook, Excel, and PowerPoint, that teams use every day. It reasons over the Graph (including your emails, meetings, and files) to act as a proactive partner in your workflow.
The questions organizations need to ask are no longer about capability ("What can it do?"), but about application ("How do we integrate this into our operational workflows?") and governance ("How do we ensure data security?").
The low-code entry point for the copilot experience is templates. As illustrated in the Copilot ecosystem architecture above, organizations need flexible entry points ranging from out-of-the-box readiness to pro-code customization. Templates serve as the bridge, allowing teams to deploy functional agents that extend the UI for AI without managing complex infrastructure.
When building with templates, two architectural components define success: knowledge and tools.
The knowledge section of the configure tab is where you define the brain of your agent. It includes storage but also fuels the Orchestrator.
When you add data here, whether public websites, SharePoint sites, or internal files, you are enabling the underlying GPT-5 real-time router to perform deep reasoning. This router analyzes the complexity of a user's prompt and decides dynamically whether to provide a quick, high-throughput answer or to engage a deeper reasoning model to formulate a plan.
By curating this source of truth, the router grounds its reasoning in your enterprise reality, minimizing hallucinations and making every answer traceable to a verified source.
Tools are the difference between a passive chatbot and an active agent. In the Microsoft ecosystem, an agent is a worker capable of multiplayer collaboration.
Think of tools as the nervous system connecting the brain (AI) to the hands (operations). By configuring tools, which can connect to over 1,200 pre-built connectors ranging from Salesforce to SAP, you transform the agent from an information retriever into a process executor. This allows the agent to act across systems, triggering workflows and updating records directly from the chat interface.
This is the core of the Copilot Control System: integrating AI into the flow of work so that it doesn't just describe the work but helps do the work.
After you remove the training wheels of templates, you enter Copilot Studio’s authoring canvas. Here, you stop configuring and start architecting.
Building a custom agent from the ground up requires understanding conversational flow not just as a script, but as logic. In this pro code environment, you can design sophisticated behaviors using three core components:
Topics: These represent the specific paths a user might take. But unlike simple chatbots, you don't just write answers. You design logic flows with conditions, branching, and loops. This allows the agent to handle complex, multi-turn conversations that adapt based on user input.
Entities and slot filling: To make the agent truly useful, it must extract structured data from unstructured conversation. Entities identify specific variables like dates, order IDs, or location names. The engine then uses slot filling to intelligently ask for missing information. Instead of rejecting a vague request, the agent knows exactly what is missing ("I can help with that order, but what is the Order ID?") and prompts the user to fill that specific slot.
Generative Answers: This node acts as your safety net. It allows the agent to handle unanticipated questions by dynamically falling back to your connected knowledge sources. This prevents the conversation from breaking when the user asks something outside your pre-defined topics and transforms "I don't know" into a helpful, grounded response generated in real-time.
This is where the possibilities become clear. By combining generative actions (doing things) with generative answers (knowing things), organizations can build agents that solve specific, high-value problems from automating internal helpdesk tickets to retrieving complex operational data from legacy systems.
Building a functional agent in a workshop is different from deploying one in a secure enterprise environment. To bridge this gap, use the Copilot Control System, a framework to ensure your agents are secure, governed, and valuable, and this three-step production roadmap:
Don't just build because you can. Build a chatbot that provides business value, such as document inquiry chatbot (estimated 4–6 hours to build) and the HR recruitment assistant (6–8 hours). They solve specific friction points and are easy-medium wins that can help you build momentum before tackling high-complexity projects like a project manager assistant (12–16 hours).
Security cannot be an afterthought. As you deploy agents, you must address three specific layers of the Copilot Control System:
Finally, define success before you write code. Are you saving time on information retrieval? Reducing ticket volume? Improving process compliance? The most successful organizations measure business value continuously, ensuring that every deployed agent contributes to the bottom line.
Moving from a pilot to a production-grade AI ecosystem requires more than just licenses. It requires a partner who understands the overlap of enterprise architecture, data security, and product delivery.
Seven Peaks Software helps organizations handle this challenge. From identifying the right business cases to engineering secure, scalable AI solutions, we turn the promise of AI into measurable business value.
Explore further: Access the full slide deck, case studies, and tools discussed during the event.
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