# 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

- 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

### 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.

## Example use cases

- **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.

## Tools we use

Azure AI Foundry, Azure OpenAI, Claude, Model Context Protocol (MCP), Semantic Kernel, Copilot Studio, Python, TypeScript.

## Frequently asked 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.

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Provider: KaizenAI, remote-first, working with clients worldwide. Contact: mrabdulbasit1999@gmail.com · +92 311 4095179 · +92 340 4949027 · https://kaizenai.systems/contact/
Page: https://kaizenai.systems/services/ai-agents/