Agentic AI
Agentic AI means AI systems that do more than answer questions. They read your data, use tools and take steps in your business systems to finish a task, while a person stays in control of the decisions that matter.
We design and build AI agents that read, decide and act in your business systems. Typical work includes document processing, triage, research and back-office automation. Every agent is grounded in your data, tightly scoped and auditable, and asks a person before it does anything important.
What you get
The results this work is designed to deliver.
- Hours of repetitive knowledge work handled by agents
- Every agent action logged and explainable
- Model-agnostic, so you are not locked to one AI vendor
How we help
Pick one, or combine them. Most work starts small and grows.
Have a specific need?Describe it in a few lines →
Agent discovery
Find the processes where an agent pays back, and rule out the ones where it would not.
Agent engineering
Tool-using agents, retrieval (RAG) over your content, and MCP servers that expose your systems safely.
Integration
Agents wired into Dataverse, Dynamics 365, Teams, email and your APIs.
Evaluation & guardrails
Test suites, approval steps, permission trimming, cost controls and monitoring.
Where it pays off
Inbox & document triage
Read incoming emails and attachments, extract data, create records and route exceptions to a person.
Sales research agent
Research prospects and draft personalised outreach for a human to approve.
Typical scenarios. Every engagement starts from your own process.
Tools we use
Agentic AI: common questions
What is the difference between a chatbot and an AI agent?
A chatbot answers questions. An AI agent can also use tools: it looks up records, fills forms, calls APIs and moves work forward across several steps. We design agents so that anything consequential, such as sending, approving or deleting, waits for a person to confirm.
Which AI models do you use?
We are model-agnostic. We work with Azure OpenAI and Azure AI Foundry, Anthropic Claude and Microsoft Copilot Studio, and choose per use case based on quality, cost, data residency and the customer’s existing Microsoft agreements.
What is MCP and why does it matter?
The Model Context Protocol (MCP) is an open standard for connecting AI models to tools and data. We build MCP servers that expose your systems, such as Dataverse or internal APIs, to AI agents in a controlled, permission-aware way, so the same integration works across several AI clients.
How do you keep AI agents safe?
Every agent is security-trimmed to the user it acts for, cites the records it used, logs its actions, needs approval for writes, and can be switched off per environment. We also test agents against evaluation sets before and after each change.
Other services
- 02 Copilot & Cowork Make Microsoft 365 Copilot, Copilot Studio and Cowork useful with your own data.
- 03 Dynamics 365 CRM Sales, Customer Service and Field Service, built around the way your team works.
- 04 Power Platform Apps, automation and portals on Dataverse, governed and built to last.
- 05 Systems Integration Connect CRM, telephony, ERP and line-of-business systems so data flows without re-keying.
- 06 Infra & DevSecOps Secure Azure foundations and pipelines that ship safely, every time.
Have a process that should run itself?
Tell us what you are trying to fix. You get an honest answer on whether, and how, Microsoft and AI can help, usually within a working day.