Deploying AI Agents with Semantic Kernel and Azure OpenAI

Marco Farina
Jan 14
6 min read

Introduction

As generative AI systems evolve, organizations are moving beyond simple prompt-response interactions toward autonomous AI agents capable of executing multi-step workflows. These agents can reason over tasks, invoke external tools, retrieve information, and coordinate complex operations across enterprise systems.

Within the Microsoft ecosystem, one of the most powerful frameworks for building AI agents is Semantic Kernel, an open-source SDK designed to orchestrate large language models with external tools, APIs, and enterprise services.

When combined with Azure OpenAI, Semantic Kernel allows developers to build intelligent agents capable of planning tasks, executing actions, and interacting with real-world systems. These agents can support use cases such as automated research assistants, internal enterprise copilots, incident response bots, and workflow automation engines.

This article explores how AI agents work, how Semantic Kernel enables agent orchestration, and how developers can deploy production-ready agents that integrate with Azure-based services.

From LLM Applications to AI Agents

Most early generative AI applications follow a simple interaction model. A user provides a prompt, the model generates a response, and the interaction ends. While this approach works for many use cases, it does not support complex problem-solving or multi-step operations.

AI agents extend this model by introducing planning and tool execution capabilities.

Instead of generating a single response, an agent can break down a user request into multiple steps. Each step may involve reasoning, retrieving information, calling external APIs, or executing functions.

For example, an enterprise IT assistant agent might receive a request such as:

“Investigate why our Azure application is experiencing high latency.”

Rather than returning a generic explanation, the agent could perform a sequence of actions:

Query monitoring metrics
Retrieve recent deployment logs
Analyze system alerts
Summarize possible root causes

This multi-step workflow is made possible by integrating LLM reasoning with programmable tools.

Overview of Semantic Kernel

Semantic Kernel is an orchestration framework designed to connect large language models with traditional software functions. It allows developers to create AI-powered workflows where LLMs can dynamically decide which tools to invoke.

The framework supports several key components.

Plugins

Plugins are collections of functions that an AI agent can call during execution. These functions may perform operations such as retrieving database records, calling REST APIs, or executing internal business logic.

Semantic Functions

Semantic functions represent prompts that interact with the language model. They allow developers to structure AI reasoning tasks such as summarization, classification, or planning.

Memory

Semantic Kernel includes memory capabilities that allow agents to store and retrieve contextual information across interactions. This feature enables persistent conversation histories and knowledge retrieval.

Planners

Planners allow the system to automatically generate action sequences for complex tasks. When given a goal, the planner determines which functions should be executed and in what order.

Together, these components create a framework for building autonomous AI agents that can interact with software systems.

Agent Architecture with Azure OpenAI

A typical AI agent architecture built with Semantic Kernel and Azure OpenAI includes several layers.

The user interface layer provides interaction channels such as chat applications, web interfaces, or messaging platforms.

The agent orchestration layer runs the Semantic Kernel engine. This layer manages conversation context, determines which tools should be used, and orchestrates task execution.

The language model layer provides reasoning capabilities. Azure OpenAI models analyze prompts, generate plans, and decide which functions should be invoked.

The tool layer consists of plugins that allow the agent to interact with external systems such as databases, APIs, monitoring tools, or document retrieval systems.

Finally, the data layer stores knowledge sources, memory records, and enterprise data that the agent may need to access.

This layered architecture separates reasoning, execution, and data access responsibilities, enabling scalable and maintainable AI agent systems.

Creating an AI Agent with Semantic Kernel

The first step in building an AI agent is initializing the Semantic Kernel runtime and connecting it to Azure OpenAI.

The kernel acts as the central orchestration engine that coordinates interactions between the LLM and available tools.

Developers configure the kernel with an Azure OpenAI deployment endpoint and authentication credentials. Once connected, the kernel can send prompts to the language model and receive responses.

After initialization, developers register plugins that expose executable functions. These plugins represent the capabilities that the agent can access during its reasoning process.

For example, an enterprise agent might include plugins that retrieve knowledge base documents, query monitoring metrics, or interact with customer databases.

By exposing these capabilities to the language model, the agent can dynamically choose which actions to perform in order to satisfy a user request.

Implementing Tool Functions

Tool functions form the operational backbone of AI agents. These functions allow the agent to interact with real-world systems.

For example, a plugin might contain functions that retrieve system metrics from monitoring services. Another plugin may interact with a ticketing system to create support requests.

When defining tool functions, developers provide descriptions that explain the purpose of each function. These descriptions are used by the language model to determine when and how to invoke the function.

During execution, the model analyzes the user request and determines whether a function should be called. If the model decides to invoke a function, the kernel executes the function and returns the result back to the model.

The model then incorporates the returned data into its reasoning process before generating the final response.

Automatic Planning and Task Decomposition

One of the most powerful capabilities of Semantic Kernel is its ability to generate execution plans automatically.

When a user submits a complex request, the planner analyzes the available tools and determines the sequence of actions required to complete the task.

For example, a request such as:

“Generate a weekly report summarizing our application performance”

may trigger a plan consisting of multiple steps.

The agent might first retrieve performance metrics from monitoring systems. Next, it may analyze error logs or latency patterns. Finally, it could generate a natural language summary of the findings.

The planner orchestrates these steps automatically, enabling the system to execute complex workflows without requiring manual scripting.

This capability transforms LLM-based applications into intelligent agents capable of performing sophisticated operations.

Memory and Context Management

AI agents often require persistent context across multiple interactions. Semantic Kernel provides memory mechanisms that allow agents to store and retrieve contextual information.

Memory systems may store user preferences, previous conversations, or domain-specific knowledge. This allows agents to maintain continuity across sessions and improve personalization.

For example, an enterprise assistant might remember the services that a user manages or the environment in which they operate.

When the user interacts with the agent again, the system retrieves relevant memory entries and incorporates them into the prompt context.

This memory-driven approach allows AI agents to behave more like long-term collaborators rather than isolated query processors.

Deploying AI Agents in Production

Deploying AI agents in production environments requires careful attention to scalability, reliability, and security.

The agent orchestration service is typically deployed within scalable compute environments such as containerized microservices or serverless platforms. These environments allow the system to handle large volumes of concurrent requests.

Load balancing mechanisms distribute incoming traffic across multiple agent instances. This prevents individual nodes from becoming performance bottlenecks.

Monitoring systems track agent behavior, including tool invocation frequency, response latency, and error rates. These metrics help developers identify performance issues and optimize system efficiency.

Security controls ensure that agents only access authorized resources. Access permissions are enforced through identity management systems and role-based access policies.

These operational safeguards ensure that AI agents remain stable and secure in enterprise environments.

Observability and Debugging

Debugging AI agents requires visibility into both model reasoning and tool execution. Developers must understand how the agent arrives at particular decisions and whether tool invocations are producing correct results.

Semantic Kernel provides tracing capabilities that allow developers to inspect execution workflows. Logs can capture prompt inputs, model outputs, and intermediate tool responses.

Observability tools also help identify situations where the agent misinterprets a request or selects the wrong function. These insights allow developers to refine prompt instructions, improve tool descriptions, or adjust planning strategies.

Continuous monitoring and debugging are essential for maintaining reliable agent behavior in production systems.

Real-World Use Cases

AI agents built with Semantic Kernel and Azure OpenAI can support a wide range of enterprise applications.

Internal enterprise copilots can assist employees by retrieving knowledge from internal documentation systems. IT operations agents can monitor infrastructure metrics and diagnose system issues. Research assistants can analyze large document collections and summarize key insights.

Customer service agents can integrate with CRM platforms to retrieve account information and generate personalized responses.

These use cases demonstrate how AI agents extend the capabilities of traditional LLM applications by combining reasoning, memory, and external system integration.

Conclusion

AI agents represent the next evolution of generative AI systems. By combining large language model reasoning with programmable tools, organizations can build intelligent systems capable of executing complex workflows and interacting with enterprise infrastructure.

Semantic Kernel provides a powerful orchestration framework that enables developers to build such agents efficiently. When integrated with Azure OpenAI, it allows organizations to deploy scalable and secure AI agents capable of supporting real-world business operations.

As AI technologies continue to advance, agent-based architectures will likely become a foundational pattern for building intelligent enterprise systems.