Looking for best architecture/approach for ai agent that take actions in saas platform

I’m currently digging deeper into how to build an AI agent from A to Z that can actually take actions inside a platform based on natural-language user requests. For example, imagine a user saying: “Reserve me a room at Hotel X for 3 nights starting next Friday.” The agent should be able to understand the request, collect any missing information, interact with the platform’s APIs/services, execute the reservation, handle failures or changes, and finally confirm the result to the user. I’m familiar with the general concepts around LLM agents, MCP, n8n, tool calling, function calling, RAG, etc., but I’m specifically looking to go beyond the usual high-level explanations. I’d love to hear about real architectures or approaches you’ve actually implemented, especially around: How do you structure the agent architecture? Do you use a single agent, multi-agent system, or an orchestrator + specialized agents? How do you expose platform capabilities to the agent: REST APIs, MCP servers, function/tool calling, SDKs, etc.? How do you handle authentication, permissions, user identity, and authorization? How do you make sure the agent cannot perform unauthorized or dangerous actions? How do you handle multi-step workflows where one action depends on the result of another? What happens when an API call fails halfway through a workflow? How do you handle state, memory, retries, idempotency, and rollback/compensation? Where do you put business logic: inside the agent, backend services, workflow engine, or a combination? How do you implement human-in-the-loop confirmation for sensitive actions such as payments, bookings, cancellations, etc.? How do you monitor and debug what the agent is doing in production? What does the architecture look like when you have hundreds of tools/actions available? I’m particularly interested in seeing concrete architecture diagrams, code examples, GitHub projects, technical articles, or production lessons learned. For example, I’d love to understand an architecture along the lines of: User → LLM/Agent → Planner/Orchestrator → Tools/APIs → Platform → Result → Agent → User …but with the actual components and engineering decisions behind each layer. If you’ve built something like this in production, I’d really appreciate hearing what worked, what didn’t, and what you would do differently today. I’m not looking for another “just use MCP/n8n” answer, I’m trying to understand the deeper engineering architecture behind reliable action-taking AI agents.