VisierとAmazon Quickを用いた workforce AIエージェントの構築

Employees across every function are expected to make faster, better-informed decisions, but the information that they need rarely lives in one place. Workforce intelligence (who is in your organization, how they are performing, and where the gaps are) is one of the most valuable signals an enterprise has, and platforms like Visier are purpose-built to surface it. However, that intelligence only reaches its full value when it’s connected to the internal policies, plans, and context that give it direction. That context also often lives somewhere else entirely.

Amazon Quick is the Agentic AI workspace where that connection happens. It brings together enterprise knowledge, business intelligence, and workflow automation. Its intelligent agents retrieve information and reason across all of these layers simultaneously, interpreting live data alongside organizational context to produce answers that are ready to act on. When Visier workforce intelligence works in tandem with the Amazon Quick enterprise knowledge layer, the result is an answer that draws on the full context and is ready to act.

In this post, we show how connecting the Visier Workforce AI platform with Amazon Quick through Model Context Protocol (MCP) gives every knowledge worker a unified agentic workspace to ask questions in. Visier helps ground the workspace in live workforce data and the organizational context that surrounds it while letting your users act on the conversational results without switching tools.

1. Understanding the components

In this post, we demonstrate example day-to-day workflows for two people preparing for the same leadership meeting: Maya, an HR Business partner building a workforce health briefing, and David, a finance manager tracking headcount against budget. Both need answers that cut across multiple sources, such as live workforce data, internal targets, hiring policies, and historical context. This integration is built for business users who work with people data as part of their day-to-day decisions. They need answers grounded in the right data sources. This integration helps Amazon Quick agents go beyond retrieving information and act on it.

Amazon Quick

Amazon Quick is an agentic AI workspace that acts as a unified interface for business users across the organization, provides business users with a set of agentic teammates that quickly answer questions at work and turn those answers into action.

For Maya and David, Amazon Quick is their AI workspace where they ask questions and build agents that work on their behalf and automate their processes. Weekly workflows and threshold alerts that would otherwise require manual effort and research every time are saved in Amazon Quick.

Visier

Visier is a cloud based Workforce AI platform that unifies workforce data from across an organization. It brings together HRIS, payroll, talent management, and applicant tracking into a single intelligence layer. You can use it to answer complex workforce questions in minutes through its AI assistant Vee, backed by extensive pre-built metrics and industry benchmarks from anonymized employee records.

Through its MCP server, Visier acts as a universal connector that delivers governed people insights directly into the enterprise AI tools where decisions are made.** For Maya, Visier is the authoritative source for workforce intelligence. It provides the high performer counts, average tenure figures, and attrition trends that she needs to assess organizational health. For David, it provides the live headcount and distribution figures that financial targets are measured against.

The Model Context Protocol**

MCP is an open standard that enables AI agents to connect to external data sources and tools. Think of it as a universal adapter that allows Amazon Quick to communicate with Visier’s analyst agent, Vee in a structured and secure way without building custom integrations from scratch. Visier exposes its workforce analytics capabilities through an MCP server. Amazon Quick includes a built-in MCP client that discovers those tools and makes them available to its agents, research workflows, and automations.

2. Benefits for enterprises

Organizations often struggle to get a unified view of their workforce that combines live data with organizational context. A manager asking “Are we on track with our headcount budget?” needs numbers from one system and policy context from another. With Visier integrated into Amazon Quick using MCP, this gap closes:

• Unified workforce intelligence – Amazon Quick orchestrates across Visier’s live people analytics data and your internal enterprise knowledge, delivering synthesized answers that neither system could produce alone. A single question can return live headcount data cross-referenced against an approved budget document.

• Natural language access to employee data – Through Amazon Quick Agents, users can ask conversational questions and get instant answers backed by curated workforce data. Every response is attributed to its source, so users always know whether a figure came from Visier’s live workforce data or an internal policy document in Quick Spaces.

• Automated, repeatable workflows – Recurring workforce reviews, threshold alerts, and pre-meeting briefings can be built as automated Quick Flows that run on a schedule. The same analysis Maya and David ran manually in the demo can be configured once and delivered to their inboxes every Monday morning without any manual effort.

• Cross-functional decision support – The same pattern applies across any function where workforce data and organizational context need to come together to inform a decision.

• Governed and secure data access – Visier’s MCP server enforces data governance policies to surface only authorized workforce data through Amazon Quick. Enterprise knowledge in Quick Spaces maintains existing access controls within your organizational boundary.

• Reduced time to insight – What previously required hours of cross-referencing spreadsheets, toggling between dashboards, and manually building narratives can now be accomplished quickly from a single interface. The integration ensures that the answer always comes with the full picture of live workforce data alongside the organizational context that makes it actionable.

3. Prerequisites

Before setting up the Visier MCP integration with Amazon Quick, you need the following:

• A valid AWS account with appropriate service permissions.

• An Amazon Quick environment with an active subscription (getting started).

• A Visier account with API access and MCP server enabled.

For more information about setting up Amazon Quick, see the Amazon Quick documentation.

4. Solution overview

At its core, this solution is built on the MCP. Visier hosts an MCP server that exposes its people analytics capabilities as a set of callable tools. Amazon Quick acts as the MCP client, discovering those tools and making them available to agents, research workflows, and automations. The two platforms remain independent, and through this connection, live workforce data from Visier becomes part of every Amazon Quick interaction.When a user asks a question:

• Amazon Quick interprets the intent and determines which sources are relevant

• If the question requires workforce data, it invokes Visier’s Vee agent through MCP to retrieve live analytics

• If the question requires organizational context, it draws from the relevant documents and knowledge sources available in Amazon Quick Spaces

• The two sources are brought together into a single, coherent response that reflects both live workforce data and the organizational context around it

When a question spans both systems, Amazon Quick identifies the right sources, hands off to Visier’s agent to retrieve live workforce intelligence, and draws on Quick Index and Quick Spaces for organizational context. The most relevant information from both is surfaced back to the user as a single, coherent answer.

5. Setting up the integration

Step 1: Configure Visier’s MCP server

Visier provides a prebuilt MCP server that exposes its workforce analytics capabilities as MCP tools. To configure it:

• In your Visier admin console, navigate to Settings > API & Integrations.

• Enable the MCP Server capability.

• Configure authentication credentials and data access scopes.

• Note the MCP server endpoint URL and authentication details.

For detailed instructions, refer to the Visier MCP Documentation.

Step 2: Add Visier as an MCP integration in Amazon Quick

Amazon Quick includes a built-in MCP client that you configure through an integration. To connect Visier:

• From the Amazon Quick home screen, select Integrations from the left navigation panel.

• Select the Actions tab in the main panel.

• Under Set up a new integration, locate the Model Context Protocol (MCP) tile and choose the plus (+) sign.

• On the Create Integration page, enter a descriptive Name, an optional Description, and the Visier MCP server endpoint URL from Step 1. Choose Next.

• Select the authentication method that matches your Visier MCP server configuration (user authentication, service authentication, or no authentication) and enter the required credentials. Choose Create and continue.

• Amazon Quick will discover the tools exposed by Visier’s MCP server (for example, ask_vee_question, search_metrics, list_analytic_object_property_values).

• Share the integration with other users who should be able to query Visier through Amazon Quick, then choose Done.

After configured, Visier workforce intelligence tools are available to the Amazon Quick agents and automations.**

For more information about MCP integration in Amazon Quick, refer to Integrate external tools with Amazon Quick Agents using MCP and the MCP integration documentation.

Step 3: Curate your enterprise knowledge

Agents built in Amazon Quick use Spaces as their contextual boundary. Everything an organization knows, from internal policies and planning documents to team-specific knowledge contributed by individual users, is built up inside a Space and made available to the agent at query time. Multiple team members can contribute to a Space over time, so the knowledge grows with the organization rather than remaining static.

Next, you upload relevant internal documents to Quick Spaces, so the orchestrator has organizational context to complement Visier’s live data. To upload your documents:

• In Amazon Quick, navigate to Spaces and create a new space. Name it “Workforce Planning“.

• Upload your workforce planning documents, such as headcount budgets, and compensation guidelines.

• Upload policy documents, such as approval workflows, and compliance requirements.

• Configure space permissions to control which teams can access the content.

With Quick Spaces populated, the answers we get from Quick Agents get richer. This lets them combine live workforce data from Visier with your organization’s own context and return a complete answer in one place.

Example scenario**

To demonstrate the integration, we walk through a scenario where Maya (HR Business Partner) and David (Finance Analyst) are preparing jointly for a leadership meeting. Their organization has connected Visier to Amazon Quick using MCP and has uploaded internal planning documents to Quick Spaces.For this example, they have added the following enterprise documents to Amazon Quick:

Document

Purpose

FY26 Workforce Health Targets

Headcount goals, US distribution targets, retention rate benchmarks

Tenure and Retention Policy

Tenure milestones, at-risk thresholds, intervention triggers

High Performer Retention Playbook

High performer ratio thresholds, retention levers, escalation triggers

US Workforce Distribution Policy

Target US presence percentage, review cadence, rationale

Workforce Risk Briefing Template

Risk rating framework, what to escalate to leadership

Here’s how the conversation unfolds:Each of the following turns note which data sources that the Amazon Quick agent queried to produce its response.

Turn 1: Getting the lay of the land

David: How many employees do we have, and how many are based in the US?

*The Amazon Quick agent routes David’s question to Visier via MCP and returns the total employee count and US-based headcount from live workforce data.*

Sources queried: Visier

Turn 2: Budget vs. actual, where intelligence meets context

David: How does our US headcount compare to our distribution targets?

The agent queries Visier for live US headcount and retrieves the FY26 Workforce Health Targets document from Quick Spaces, comparing the actual figure against the approved distribution target.

Sources queried: Visier (live headcount) · Quick Spaces (FY26 Workforce Health Targets)

Turn 3 : Tenure landscape

Maya: What is the average tenure across our workforce, and which roles have the highest tenure?

The Amazon Quick agent retrieves average tenure and role-level tenure breakdowns from Visier, then surfaces the relevant tenure milestones from the Tenure and Retention Policy in Quick Spaces.

Sources queried: Visier (tenure data) · Quick Spaces (Tenure and Retention Policy)

Turn 4 : Tenure against policy thresholds

Maya: Does our average tenure meet the threshold in our retention policy?

The Amazon Quick agent compares Visier’s live average tenure figur