Artificial Intelligence is rapidly evolving. Microsoft is predicting that more than 1 billion agents will be used by 2028 [1]. Additionally, businesses are feeling the pressure to increase productivity, gain a competitive advantage through automation, and improve the work-life balance of their employees. AI will not be able to solve all challenges, but with experimentation, it can be an important tool.
At Omnitech, we’ve been pragmatically researching many aspects of AI to understand the feasibility, capabilities, and how our clients can adopt AI agents in ways that are secure, cost-effective, and align with business goals.
AI agent systems go beyond chatbots. Unlike Generative AI, AI Agents are autonomous software systems that perceive their environment, make decisions based on goals, and take actions—without constant human intervention. They learn from feedback and adapt over time.
A well-planned and targeted agent can perform tasks such as:
· Pull information from multiple systems
· Make decisions based on business rules
· Execute multi‑step workflows
· Coordinate with other agents or services
· Act on behalf of a user (with proper identity and permissions)
This makes agents a powerful tool for modern organizations looking to automate processes to reduce repetitive work, improve efficiency, and unlock insights hidden across siloed data and systems.
With the exponential growth of data and the numerous systems we use to handle this data, we need to find better ways of reducing the fragmentation of these data sources. AI agents can unify these sources, such as:
· SharePoint
· Databases
· Microsoft Graph / M365
· OneNote
· Internal APIs
Many clients already struggle with “information everywhere.” Agents help connect the dots. This provides insights into your data that would otherwise go unnoticed without AI.
Across industries, organizations are seeking systems that automate routine work so their employees can focus on more value-added tasks. Having AI agents as part of the workflow can streamline tasks such as HR questions, PTO requests, onboarding, scheduling, and knowledge lookups. This allows employees to focus on innovation while at the same time improving internal communications, saving time, and reducing errors.
With Generative AI, analyzing your data is non-deterministic, which can lead to hallucination or potentially incorrect answers. With agents, it can improve this by delivering consistent answers, escalating when needed, and personalizing interactions based on role and identity. This provides better service without additional staffing.
With intelligent retrieval-augmented generation (RAG) using services like Foundry IQ, agents can ground their responses by using your own company knowledge - securely and in real time. This is done through direct connections to your data through tools, such as Model Context Protocol (MCP), which can maintain secure authentication of the users interacting with the agents.
AI is not a one-size-fits-all approach. We would first want to evaluate if an AI Agent is the correct tool for your needs.
We can guide the discovery of business needs through strategic planning and experimentation.
We can help you determine your high-value use cases and structure the AI Agents that functionally target each one.
We will evaluate existing solutions and other deterministic code to know if it might be a better fit than using AI.
Our team can help you evaluate your data readiness and make it usable for AI Agents and other potential advanced analytics strategies.
We can help guide you through key decisions:
When to use built‑in tools vs. custom code;
How to design security with Role-Based Access Control (RBAC)
How to structure internal knowledge sources;
How to manage costs (AI Search vs vector DB, hosted vs on‑premise/local models);
How to comply with regulations and ethics;
How to evaluate risks of using AI;
How to roll out agents safely and progressively.
Changes with AI are happening fast, so we’ve spent the last several months evaluating:
Azure AI Foundry Agents;
Microsoft Agent Framework (MAF);
Secure identity models using Entra Agent ID (delegated & unattended modes);
Custom C# and Azure Function agents;
Vector databases & enterprise RAG pipelines;
Teams and web app integrations;
Orchestration workflows for multi‑step processes.
AI agents are powerful, but they introduce new challenges and questions. We help address these challenges through a balanced approach:
Well Governed
How do we secure agents?
How do we verify outcomes and build trust in the results?
Practical
How do we experiment and evaluate to discover optimal solutions?
How can we fit the right AI solution based on your readiness?
Measurable
How do we compare AI-generated summaries to expert knowledge?
How can we measure the risk to our business?
Maintainable
How do we evaluate when the agent is ready for production?
How do we continuously monitor AI implementations?
Cost-conscious
How do we weigh economic limitations?
How do we measure value and ROI
Integrated with your existing M365 and Azure environment
What data should they access?
Do we need custom code, or can we use Microsoft’s tools?
Let’s talk about how AI agents can simplify workflows, reduce operational friction, provide value to your clients, and help your teams do more of what matters.
