Agentic AI Solutions Architecture .NET

This white paper is designed to align an extensive background in distributed systems with the specific requirements of an Agentic AI architecture role. It bridges the experience in .NET, Azure, and DDD with the cutting-edge patterns required for autonomous AI systems.

White Paper: Architecting Agentic AI Systems in .NET & Azure

Prepared for: Senior Solutions Architect

Subject: Technical Foundations for Market-Leading Agentic AI in Pharma

1. Core Concepts: Beyond Simple LLMs

While standard AI follows a linear path (Input → LLM → Output), Agentic AI introduces a “reasoning loop” where the system determines its own execution path.

• The Reasoning Loop: The core engine (usually an LLM) interprets a goal, breaks it into steps, executes a tool, observes the result, and iterates until the goal is met.
• Tools & Plugins: Discrete units of logic (e.g., a .NET Web API or a SQL query) that an agent can “call.”
• Planning: The ability of an agent to create a multi-step sequence of actions before executing them.
• State & Memory: Maintaining context across long-running “conversations” or complex scientific workflows.

2. Architectural Patterns for Agentic Systems

A. The Orchestrator Pattern (Semantic Kernel)

In the .NET ecosystem, Semantic Kernel (SK) is the primary framework. It treats the LLM as a “kernel” that orchestrates “plugins” (your existing C# code).

• Implementation: Define your business logic as [KernelFunction] decorated methods.
• Relevance: Ideal for pharma workflows where an agent needs to access specific clinical trial databases or regulatory APIs.

B. Multi-Agent Systems (MAS) & Conversational Agents (AutoGen)

This involves multiple specialized agents (e.g., a “Researcher Agent” and a “Compliance Agent”) talking to each other to solve a problem.

• Pattern: One agent proposes a drug dosage; the second agent (Compliance) critiques it against FDA guidelines.
• Implementation: Use AutoGen.NET or Microsoft’s Orleans for distributed state management to handle agent-to-agent communication at scale.

C. The “Small Agent” Loop (smolagents/Minimalist)

Focuses on lightweight, code-first agents that treat code generation as their primary reasoning tool.

• Pattern: The agent writes and executes temporary Python or C# scripts to perform complex calculations or data analysis.

3. The .NET & Azure Implementation Stack

Infrastructure as Code (Bicep/Terraform)

For a pharmaceutical product, your IaC must provision a “Secure AI Landing Zone.”

• Resources: Azure OpenAI Service, Azure AI Search (Vector Store), and Azure Container Apps (to host the agents).

Luis’s Edge: Use your 15+ years of experience to discuss how you automate the deployment of Provisioned Throughput (PTU) to ensure low latency for time-critical pharma research.

Identity & Security

• Managed Identity: Use Azure Entra ID (Managed Identities) so your .NET services never handle raw API keys for Azure OpenAI.
• Data Residency: Ensure data processed by agents stays within specific Azure regions to comply with healthcare regulations (GDPR/HIPAA).

Guardrails & Safety

• Azure AI Content Safety: Use this to filter harmful outputs or sensitive medical misinformation.
• Semantic Guardrails: Implement custom .NET logic to validate that an agent’s output adheres to a specific JSON schema or a medical ontology before it reaches the UI.

Monitoring & Observability

• OpenTelemetry: Standardize your agentic traces.
• App Insights (AI Lens): Track “Token Usage” and “Reasoning Latency.”
• Traceability: In Pharma, you must be able to “audit” why an agent made a specific decision. Store the full reasoning chain (the “Thought” process) in Azure Cosmos DB.

4. Testing the Non-Deterministic

Testing agents is harder than testing standard APIs because the “answer” can change.

• LLM-as-a-Judge: Use a stronger model (GPT-4o) to grade the outputs of your agentic model.
• Prompt Flow: Use Azure AI Studio’s Prompt Flow to run bulk tests against “Golden Datasets” of medical queries to ensure no regression in accuracy.