Agentic AI Development
Autonomous AI Agents for Enterprise Automation
Live Agent Demos
Live Demo: Text-to-SQL
Ask a natural language question about WoundCareDB
Live Demo: Cloud-Optimizer-Agent (Azure AI Foundry)
Describe your cloud setup (provider, monthly spend, whether you have a Lakehouse) and the agent will call its check_architecture_cost tool to estimate potential savings.
Live Demo: Lanacloud Advisory Copilot
A conversational agent built with Microsoft Copilot Studio, using Generative Answers grounded on lanacloud.com content.
RAG Agent with Microsoft Foundry & SharePointComing Soon
Our next agent in development: a Retrieval-Augmented Generation (RAG) agent deployed on Microsoft Foundry, authenticated against SharePoint via Microsoft Entra ID to securely query enterprise documents.
- Microsoft Entra ID delegated authentication to access SharePoint on behalf of the signed-in user
- Document retrieval grounded on SharePoint document libraries, with source citations
- Access control that respects native SharePoint permissions (no unauthorized document is ever exposed)
- Built on Microsoft Foundry, connectable to other agents (e.g. Cloud-Optimizer-Agent) as a future multi-agent workflow
This demo will be published here once it's ready.
Agentic AI represents the next evolution of artificial intelligence - autonomous systems that can perceive their environment, make decisions, and take actions to achieve specific goals. Unlike traditional AI, agentic systems can plan multiple steps ahead, use tools, and adapt to changing situations.
We build AI agents with:
| Platform | Description |
|---|---|
| Azure AI Agent Service | Enterprise agent framework with built-in security |
| LangChain & LangGraph | Open-source frameworks for building LLM applications |
| Semantic Kernel | Microsoft's SDK for integrating LLMs into apps |
| AutoGen | Multi-agent framework for complex collaborative systems |
Autonomous Agents
| Capability |
|---|
| Task planning and execution |
| Decision-making and problem-solving |
| Learning and adaptation |
| Tool usage and API integration |
| Memory and context management |
Multi-Agent Systems
| Capability |
|---|
| Collaborative agents with specialized roles |
| Agent coordination and communication |
| Distributed problem-solving |
| Hierarchical agent systems |
| Consensus and voting mechanisms |
Enterprise Integration
| Capability |
|---|
| Integration with existing systems |
| Role-based access control |
| Audit logging and traceability |
| Monitoring and observability |
| Production-ready deployments |
| Use Case | Description |
|---|---|
| Intelligent Customer Support | Agents that handle complex inquiries end-to-end |
| Process Automation | Automate multi-step workflows across departments |
| Research Assistant | Agents that research, analyze, and synthesize information |
| Code Generation | AI assistants that write and review code |
| Data Analyst | Agents that explore, analyze, and visualize data |
| Project Manager | AI agents that plan, track, and coordinate projects |
| # | Step |
|---|---|
| 1 | Identify use cases and define agent objectives |
| 2 | Design agent architecture and select tools |
| 3 | Develop and train agents with your data |
| 4 | Rigorously test for safety and reliability |
| 5 | Deploy with monitoring and continuous improvement |