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We Built Agents using Microsoft Frameworks That Actually Run in Production

Lakshya Jain
By Lakshya Jain
Dec 02, 2025
We Built Agents using Microsoft Frameworks That Actually Run in Production

AI has moved past the hype cycle and into a decisive phase. Enterprises are no longer impressed by demos or prototypes. They want solutions that can plug into real systems, support real workflows, and deliver real business outcomes. And this is exactly where the conversation around AI agents has taken a serious turn.

Enterprises want AI that works inside real systems, not just in demos, and we have successfully built and deployed agents that operate in Microsoft Teams and Copilot at production scale.

Enterprises today are shifting rapidly from AI experimentation to full-scale deployment. Executives are no longer asking, “Can we use AI?”
They are asking, “How do we integrate AI into our existing workflows, systems, and teams in a reliable and secure way?”

This is where many organizations hit a wall.

Building an intelligent agent in a lab is one thing.
Deploying it inside Microsoft Teams, Microsoft Copilot, or enterprise applications at scale is an entirely different challenge.

And that is exactly the journey our team has been leading.

Over the past year, we have moved from early-stage proof of concepts to delivering fully production-ready AI agents that operate inside real enterprise environments. We have built agents using Microsoft Semantic Kernel, Microsoft Agent Framework, LangChain, and other orchestration tools. Through this work, we have seen how these technologies bridge the gap between AI capabilities and real business needs.

Semantic Kernel continues to be a critical part of our architecture, providing the orchestration foundation needed for maintainability, scalability, and extensibility.

1. Why Semantic Kernel Is the Backbone of Enterprise AI Agent Development

Semantic Kernel enables AI systems to work with enterprise applications without disruption. For agent-based systems, this orchestration layer is essential.

Through our implementation work, we have seen how Semantic Kernel supports:

  • Smooth integration with legacy and modern platforms

  • Centralized orchestration logic for multi-agent workflows

  • Plugin-based extensibility that accelerates development

  • Production-ready governance and maintainability

This means AI agents do not sit on the side as isolated tools. They become operational digital co-workers embedded in real systems.

2. Our Journey From POCs to Production-Ready AI Agents

Most companies stop at experimentation.
We continued through to full deployment.

Below is a summary of what we have built.

A. AI Agents Using Semantic Kernel, Microsoft Agent Framework, and LangChain

We have delivered multi-agent ecosystems using:

  • Microsoft Semantic Kernel as the orchestration layer

  • Microsoft Agent Framework for standardized agent structure

  • LangChain for reasoning and tool coordination

  • Azure OpenAI and other LLM services for generative intelligence

These agents now support tasks such as:

  • Multi-step planning

  • Task decomposition

  • Enterprise data retrieval

  • Knowledge reasoning

  • API-driven action execution

B. Taking AI Agents From Pilot to Production

Scaling prototypes into production required addressing challenges such as:

  • Security and identity governance

  • Observability and monitoring

  • Performance optimization

  • Reliability of multi-agent workflows

  • Plugin maintainability

  • Integration with enterprise APIs

  • Human validation checkpoints

Today, our agents are supporting real business operations, not just test environments.

C. Agents Integrated Directly Into Microsoft Teams and Microsoft Copilot

One of our major achievements is building fully operational agents inside Microsoft Teams and Copilot using pro-code solutions.

These agents:

  • Support employees directly inside their daily collaboration tools

  • Trigger workflows and gather insights

  • Retrieve knowledge from enterprise repositories

  • Generate summaries, reports, and recommendations

  • Execute system-level tasks through plugins and connectors

This approach ensures high adoption because employees do not need to change the way they work.

D. Using Semantic Kernel as the Orchestration Layer for Long-Term Scalability

To ensure consistent architecture, we use Semantic Kernel as the foundation for:

  • Memory management

  • Skill and plugin organization

  • Planning capabilities

  • Reusable workflows

  • Integration patterns

  • Model-agnostic orchestration

This is what allows our solutions to evolve without complete rewrites.

3. The Business Value We Are Delivering With Enterprise AI Agents

Based on our production deployments, AI agents are driving value in areas such as:

IT Operations

  • Incident classification

  • Automated diagnosis

  • Recommendation and remediation assistance

Knowledge Management

  • Conversational search across documents and systems

  • Automated summarization

  • Fast retrieval of organizational knowledge

Workflow Automation

  • Streamlining approval processes

  • Triggering ERP and CRM actions

  • Coordinating tasks between departments

Employee Productivity

  • Drafting documentation and reports

  • Generating meeting summaries

  • Automating repetitive daily tasks inside Teams

These are not theoretical examples. These are use cases we have taken from pilot to production.

Actionable Insights: How Enterprises Can Start Their Own Agent Journey

1. Begin With a Focused Use Case

Select a workflow that is high-effort and well understood.
Examples include knowledge retrieval, IT incident classification, or automated document creation.

2. Invest Early in a Plugin Strategy

Plugins become reusable skills for every agent your organization builds.
This creates long-term scalability.

3. Standardize on Semantic Kernel for Orchestration

This provides consistency across agents and reduces complexity as the ecosystem grows.

4. Work With a Partner Who Has Delivered Production-Ready Agents

Enterprise-grade AI requires expertise in:

  • Agent frameworks

  • AI orchestration

  • Microsoft ecosystem integration

  • Security and governance

  • Production deployment disciplines

Our team brings this end-to-end capability, from design to deployment to continuous improvement.

Conclusion

AI agents are quickly becoming a core part of enterprise operations.
But real value comes only when these agents move from experimentation into production environments.

By leveraging Microsoft Semantic Kernel, Microsoft Agent Framework, and LangChain, we have successfully built and deployed AI agents inside Microsoft Teams and Microsoft Copilot using pro-code, enterprise-ready architectures. These systems are already powering real workflows and supporting real users.

The organizations that act now will gain a significant advantage.
We are excited to help shape this next chapter in enterprise AI adoption.

4. Call-to-Action

If your organization is exploring AI agents or preparing to move from prototypes to production, we would be glad to collaborate.
Connect with us for more insights on enterprise AI, agent orchestration, and Microsoft-based digital transformation.

TAGS AI dotnet AI-Development Code-With-AI AI-Programming AI-in-Enterprise AI-Transformation AI-Integration Generative-AI Microsoft-AI
Lakshya Jain

About the Author

Lakshya Jain

As an AI Engineer, I specialize in architecting and deploying intelligent agent systems that integrate seamlessly with enterprise ecosystems. My work spans multi-agent design, orchestration frameworks, LLM optimization, and production-level AI engineering. I enjoy sharing insights and helping teams navigate the rapidly evolving world of applied AI.

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