Constructing Workforce AI Brokers with Visier and Amazon Fast

Staff throughout each operate are anticipated to make sooner, better-informed selections, however the info that they want hardly ever lives in a single place. Workforce intelligence (who’s in your group, how they’re performing, and the place the gaps are) is among the most useful alerts an enterprise has, and platforms like Visier are purpose-built to floor it. Nevertheless, that intelligence solely reaches its full worth when it’s linked to the interior insurance policies, plans, and context that give it route. That context additionally typically lives some place else totally.

Amazon Fast is the Agentic AI workspace the place that connection occurs. It brings collectively enterprise information, enterprise intelligence, and workflow automation. Its clever brokers retrieve info and motive throughout all of those layers concurrently, decoding reside information alongside organizational context to provide solutions which might be able to act on. When Visier workforce intelligence works in tandem with the Amazon Fast enterprise information layer, the result’s a solution that pulls on the complete context and is able to act.

On this put up, we present how connecting the Visier Workforce AI platform with Amazon Fast by means of Mannequin Context Protocol (MCP) provides each information employee a unified agentic workspace to ask questions in. Visier helps floor the workspace in reside workforce information and the organizational context that surrounds it whereas letting your customers act on the conversational outcomes with out switching instruments.

1. Understanding the elements

On this put up, we reveal instance day-to-day workflows for 2 individuals getting ready for a similar management assembly: Maya, an HR Enterprise companion constructing a workforce well being briefing, and David, a finance supervisor monitoring headcount in opposition to finances. Each want solutions that reduce throughout a number of sources, resembling reside workforce information, inside targets, hiring insurance policies, and historic context. This integration is constructed for enterprise customers who work with individuals information as a part of their day-to-day selections. They want solutions grounded in the suitable information sources. This integration helps Amazon Fast brokers transcend retrieving info and act on it.

Amazon Fast

Amazon Fast is an agentic AI workspace that acts as a unified interface for enterprise customers throughout the group, supplies enterprise customers with a set of agentic teammates that rapidly reply questions at work and switch these solutions into motion.

For Maya and David, Amazon Fast is their AI workspace the place they ask questions and construct brokers that work on their behalf and automate their processes. Weekly workflows and threshold alerts that might in any other case require guide effort and analysis each time are saved in Amazon Fast.

Visier

Visier is a cloud based mostly Workforce AI platform that unifies workforce information from throughout a corporation. It brings collectively HRIS, payroll, expertise administration, and applicant monitoring right into a single intelligence layer. You need to use it to reply complicated workforce questions in minutes by means of its AI assistant Vee, backed by intensive pre-built metrics and business benchmarks from anonymized worker data.

By means of its MCP server, Visier acts as a common connector that delivers ruled individuals insights straight into the enterprise AI instruments the place selections are made.

For Maya, Visier is the authoritative supply for workforce intelligence. It supplies the excessive performer counts, common tenure figures, and attrition traits that she must assess organizational well being. For David, it supplies the reside headcount and distribution figures that monetary targets are measured in opposition to.

The Mannequin Context Protocol

MCP is an open commonplace that permits AI brokers to connect with exterior information sources and instruments. Consider it as a common adapter that permits Amazon Fast to speak with Visier’s analyst agent, Vee in a structured and safe approach with out constructing customized integrations from scratch. Visier exposes its workforce analytics capabilities by means of an MCP server. Amazon Fast features a built-in MCP shopper that discovers these instruments and makes them accessible to its brokers, analysis workflows, and automations.

2. Advantages for enterprises

Organizations typically battle to get a unified view of their workforce that mixes reside information with organizational context. A supervisor asking “Are we on monitor with our headcount finances?” wants numbers from one system and coverage context from one other. With Visier built-in into Amazon Fast utilizing MCP, this hole closes:

• Unified workforce intelligence – Amazon Fast orchestrates throughout Visier’s reside individuals analytics information and your inside enterprise information, delivering synthesized solutions that neither system may produce alone. A single query can return reside headcount information cross-referenced in opposition to an authorized finances doc.
• Pure language entry to worker information – By means of Amazon Fast Brokers, customers can ask conversational questions and get instantaneous solutions backed by curated workforce information. Each response is attributed to its supply, so customers at all times know whether or not a determine got here from Visier’s reside workforce information or an inside coverage doc in Fast Areas.
• Automated, repeatable workflows – Recurring workforce critiques, threshold alerts, and pre-meeting briefings could be constructed as automated Fast Flows that run on a schedule. The identical evaluation Maya and David ran manually within the demo could be configured as soon as and delivered to their inboxes each Monday morning with none guide effort.
• Cross-functional resolution assist – The identical sample applies throughout any operate the place workforce information and organizational context want to come back collectively to tell a call.
• Ruled and safe information entry – Visier’s MCP server enforces information governance insurance policies to floor solely licensed workforce information by means of Amazon Fast. Enterprise information in Fast Areas maintains present entry controls inside your organizational boundary.
• Decreased time to perception – What beforehand required hours of cross-referencing spreadsheets, toggling between dashboards, and manually constructing narratives can now be completed rapidly from a single interface. The mixing ensures that the reply at all times comes with the complete image of reside workforce information alongside the organizational context that makes it actionable.

3. Stipulations

Earlier than organising the Visier MCP integration with Amazon Fast, you want the next:

For extra details about organising Amazon Fast, see the Amazon Fast documentation.

4. Answer overview

At its core, this answer is constructed on the MCP. Visier hosts an MCP server that exposes its individuals analytics capabilities as a set of callable instruments. Amazon Fast acts because the MCP shopper, discovering these instruments and making them accessible to brokers, analysis workflows, and automations. The 2 platforms stay impartial, and thru this connection, reside workforce information from Visier turns into a part of each Amazon Fast interplay.When a person asks a query:

1. Amazon Fast interprets the intent and determines which sources are related
2. If the query requires workforce information, it invokes Visier’s Vee agent by means of MCP to retrieve reside analytics
3. If the query requires organizational context, it attracts from the related paperwork and information sources accessible in Amazon Fast Areas
4. The 2 sources are introduced collectively right into a single, coherent response that displays each reside workforce information and the organizational context round it

When a query spans each programs, Amazon Fast identifies the suitable sources, palms off to Visier’s agent to retrieve reside workforce intelligence, and attracts on Fast Index and Fast Areas for organizational context. Essentially the most related info from each is surfaced again to the person as a single, coherent reply.

5. Organising the combination

Step 1: Configure Visier’s MCP server

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

1. In your Visier admin console, navigate to Settings > API & Integrations.
2. Allow the MCP Server functionality.
3. Configure authentication credentials and information entry scopes.
4. Word the MCP server endpoint URL and authentication particulars.

For detailed directions, confer with the Visier MCP Documentation.

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

Amazon Fast features a built-in MCP shopper that you simply configure by means of an integration. To attach Visier:

1. From the Amazon Fast house display screen, choose Integrations from the left navigation panel.
2. Choose the Actions tab in the principle panel.
3. Underneath Arrange a brand new integration, find the Mannequin Context Protocol (MCP) tile and select the plus (+) signal.
4. On the Create Integration web page, enter a descriptive Identify, an elective Description, and the Visier MCP server endpoint URL from Step 1. Select Subsequent.

5. Choose the authentication methodology that matches your Visier MCP server configuration (person authentication, service authentication, or no authentication) and enter the required credentials. Select Create and proceed.

6. Amazon Fast will uncover the instruments uncovered by Visier’s MCP server (for instance, ask_vee_question, search_metrics, list_analytic_object_property_values).
7. Share the combination with different customers who ought to be capable to question Visier by means of Amazon Fast, then select Achieved.

After configured, Visier workforce intelligence instruments can be found to the Amazon Fast brokers and automations.

For extra details about MCP integration in Amazon Fast, confer with Combine exterior instruments with Amazon Fast Brokers utilizing MCP and the MCP integration documentation.

Step 3: Curate your enterprise information

Brokers inbuilt Amazon Fast use Areas as their contextual boundary. All the pieces a corporation is aware of, from inside insurance policies and planning paperwork to team-specific information contributed by particular person customers, is constructed up inside a House and made accessible to the agent at question time. A number of group members can contribute to a House over time, so the information grows with the group moderately than remaining static.

Subsequent, you add related inside paperwork to Fast Areas, so the orchestrator has organizational context to enhance Visier’s reside information. To add your paperwork:

1. In Amazon Fast, navigate to Areas and create a brand new house. Identify it “Workforce Planning“.
2. Add your workforce planning paperwork, resembling headcount budgets, and compensation pointers.
3. Add coverage paperwork, resembling approval workflows, and compliance necessities.
4. Configure house permissions to manage which groups can entry the content material.

With Fast Areas populated, the solutions we get from Fast Brokers get richer. This lets them mix reside workforce information from Visier together with your group’s personal context and return an entire reply in a single place.

Instance state of affairs

To reveal the combination, we stroll by means of a state of affairs the place Maya (HR Enterprise Accomplice) and David (Finance Analyst) are getting ready collectively for a management assembly. Their group has linked Visier to Amazon Fast utilizing MCP and has uploaded inside planning paperwork to Fast Areas.For this instance, they’ve added the next enterprise paperwork to Amazon Fast:

Doc
Function

FY26 Workforce Well being Targets
Headcount objectives, US distribution targets, retention price benchmarks

Tenure and Retention Coverage
Tenure milestones, at-risk thresholds, intervention triggers

Excessive Performer Retention Playbook
Excessive performer ratio thresholds, retention levers, escalation triggers

US Workforce Distribution Coverage
Goal US presence share, evaluate cadence, rationale

Workforce Threat Briefing Template
Threat score framework, what to escalate to management

Right here’s how the dialog unfolds:Every of the next turns observe which information sources that the Amazon Fast agent queried to provide its response.

Flip 1: Getting the lay of the land

David: What number of staff do we now have, and what number of are based mostly within the US?

The Amazon Fast agent routes David’s query to Visier by way of MCP and returns the full worker depend and US-based headcount from reside workforce information.

Sources queried: Visier

Flip 2: Price range vs. precise, the place intelligence meets context

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

The agent queries Visier for reside US headcount and retrieves the FY26 Workforce Well being Targets doc from Fast Areas, evaluating the precise determine in opposition to the authorized distribution goal.

Sources queried: Visier (reside headcount) · Fast Areas (FY26 Workforce Well being Targets)

Flip 3 : Tenure panorama

Maya: What’s the common tenure throughout our workforce, and which roles have the best tenure?

The Amazon Fast agent retrieves common tenure and role-level tenure breakdowns from Visier, then surfaces the related tenure milestones from the Tenure and Retention Coverage in Fast Areas.

Sources queried: Visier (tenure information) · Fast Areas (Tenure and Retention Coverage)

Flip 4 : Tenure in opposition to coverage thresholds

Maya: Does our common tenure meet the brink in our retention coverage?

The Amazon Fast agent compares Visier’s reside common tenure determine in opposition to the brink outlined within the Tenure and Retention Coverage saved in Fast Areas, flagging whether or not the group meets or falls in need of its goal.

Sources queried: Visier (common tenure) · Fast Areas (Tenure and Retention Coverage)

Flip 5 : Excessive Performer well being test

Maya: What number of excessive performers do we now have, and are we throughout the advisable ratio?

The Fast agent pulls the present excessive performer depend from Visier and checks it in opposition to the advisable ratio within the Excessive Performer Retention Playbook from Fast Areas.

Sources queried: Visier (excessive performer depend) · Fast Areas (Excessive Performer Retention Playbook)

Flip 6 : Management briefing synthesis

David and Maya: Summarize the important thing workforce well being dangers for our management briefing.

The Amazon Fast agent pulls collectively the workforce information retrieved from Visier throughout the prior turns) and cross-references every metric in opposition to the corresponding thresholds and insurance policies saved in Fast Areas. The place a metric falls in need of its goal, the agent flags it as a threat and surfaces the advisable motion from the related coverage doc. The result’s a single briefing that covers each dimension mentioned within the dialog, with every discovering attributed to its information supply.

Sources queried: Visier (all workforce information from prior turns) · Fast Areas (all coverage and goal paperwork)

Taking it additional with Fast Flows

Past conversational queries, Amazon Fast consists of Fast Flows, a workflow automation engine that you should utilize to outline multi-step sequences and run them on a schedule or on demand. A move can retrieve information from linked sources, apply logic or comparisons, generate formatted outputs, and ship outcomes to a vacation spot like an inbox or Slack channel, all with out guide intervention. If you end up repeating the identical multi-turn dialog with a Fast Agent each week or month, Fast Flows turns that dialog right into a self-running move. You outline the steps as soon as, join your information sources by means of the identical MCP integrations utilized in chat, and set a cadence. From there, the move executes finish to finish and delivers the end result.

The multi-turn dialog Maya and David accomplished demonstrates the type of recurring workflow that advantages from automation. Each month, the identical questions come up. How shut are we to our headcount goal? Is tenure trending in the suitable route? Is the excessive performer ratio holding? Somewhat than operating by means of these questions manually every time, Fast Flows can execute your complete sequence on a schedule and ship a ready-to-share briefing.

The next move, referred to as Weekly Workforce Well being Rating, runs each Monday morning. It retrieves reside information from Visier, compares every metric in opposition to the thresholds saved in Fast Areas, computes a composite rating, and drafts a formatted briefing, with none guide enter.

Pattern Immediate to create a weekly Workforce Well being Rating move like beneath :

Run this move each Monday at 8:00 AM. Execute the next steps in sequence:

Step 1 — Retrieve reside workforce information

Question the linked Visier MCP server for the next 4 metrics as of the latest accessible date:

1. Whole international headcount

2. US-based headcount

3. Group-wide common tenure

4. Whole depend of high-performing staff

Step 2 — Retrieve inside targets and thresholds

Search the “Workforce Planning” house in Amazon Fast for the next values:

1. 12 months-end headcount goal

2. US headcount goal and share goal

3. Common tenure threshold and watch zone decrease certain

4. Minimal excessive performer ratio threshold

Use the FY26 Workforce Well being Targets, Tenure and Retention Coverage, Excessive Performer Retention Playbook, and US Workforce Distribution Coverage paperwork.

Step 3 — Calculate workforce well being metrics

Utilizing the values retrieved in Steps 1 and a pair of, calculate the next:

1. Headcount share to aim

2. Hires remaining to shut the hole

3. US headcount share of complete

4. US headcount hole to focus on (in headcount and share factors)

5. Excessive performer ratio

6. Excessive performer buffer above the minimal threshold

7. Tenure buffer above the watch zone threshold

Step 4 — Rating every metric

Assign a rating to every of the 4 metrics utilizing the next logic:

– On Observe (meets or exceeds goal): 25 factors

– Wants Consideration (inside 5% of threshold): 15 factors

– Beneath Goal (threshold not met): 5 factors

– Wants Rapid Evaluation (considerably beneath threshold): 0 factors

Sum the 4 scores to provide a composite Workforce Well being Rating out of 100.

Step 5 — Retrieve advisable actions for flagged metrics

For any metric scored at “Wants Consideration” or beneath, retrieve the related intervention part from the corresponding Fast Areas coverage doc.

Step 6 — Draft a formatted briefing

Compose a structured abstract containing:

1. The composite rating out of 100

2. A desk displaying every metric with its precise worth, goal, calculated hole, and rating

3. A one-line standing summarizing what number of metrics want consideration

4. The advisable actions from Step 5 listed by precedence

Format this as a ready-to-share briefing.

The output is a composite rating out of 100, a metric desk displaying the place the group stands in opposition to every goal, and a set of advisable actions drawn straight from the related coverage paperwork. When a metric wants consideration, the briefing tells you what the coverage says to do about it.

After your enterprise integrations are linked, an elective step can robotically ship this briefing to a specified inbox or Slack channel on schedule. That is what Fast Flows makes attainable, a recurring, multi-source workflow that beforehand required a guide dialog turns into one thing that runs itself and exhibits up in your inbox.

Instance Fast Analysis mission

Amazon Fast additionally consists of Fast Analysis, a deep evaluation functionality designed for questions that span a number of sources and require synthesis moderately than a single lookup. The place a chat dialog is interactive and iterative, Fast Analysis runs autonomously you describe the result you want in pure language, and Fast determines which inside information bases, linked information sources, and exterior references to question, then assembles a structured, source-attributed report.

Earlier than the management assembly, Maya launches a Fast Analysis independently, exterior the agent dialog. She doesn’t specify which programs to look or the place the info lives, she simply describes what she wants.

Maya’s Fast Analysis immediate:

Put together a workforce benchmarking report forward of our management assembly. I would like to know how our group compares to business friends throughout three areas: worker tenure, excessive performer ratios, and workforce distribution throughout geographies. For every space, present me the place we stand in the present day, what the business norm appears to be like like, and whether or not we’re forward, at par, or behind. Embody our inside targets the place related.

Construction the output as an government abstract, a side-by-side benchmark comparability with color-coded threat scores, and a spot evaluation with three to 5 prioritized suggestions. Embody a benchmark comparability chart and a visible hole indicator desk. Cite all exterior sources and attribute all inside information to its origin.

Fast Analysis robotically attracts from all three layers, reside workforce information from Visier utilizing the MCP server, inside coverage targets from the Workforce Planning Fast House, and exterior business benchmarks from the online, and produces a structured, source-attributed analysis temporary. The report is downloaded by Maya and shared with David earlier than the assembly. It serves because the exterior context layer that enriches the agent dialog, giving each personas a shared start line grounded in information from inside and outdoors the group.That is what makes Fast Analysis distinct: the person describes the result that they want, Fast’s intelligence is aware of the place to look and does deep analysis, and brings an actional complete report collectively.

Monitoring and observability

As Fast brokers question Visier MCP for reside workforce information and retrieve insurance policies from Fast Areas, directors want visibility into what’s being accessed, how typically, and by whom. Amazon Fast integrates with Amazon CloudWatch to floor MCP motion connector metrics resembling invocation counts and error charges, so groups can monitor how continuously Visier’s MCP instruments are referred to as throughout agent conversations, flows, and analysis runs. Each chat interplay, together with which connectors had been invoked and which sources had been cited within the response, could be delivered by means of Amazon CloudWatch Logs to locations like Amazon Easy Storage Service (Amazon S3) or Amazon Information Firehose for evaluation and long-term retention. For audit and compliance, AWS CloudTrail supplies an entire report of API calls and administrative actions throughout the Amazon Fast surroundings, answering questions like which person queried workforce tenure information, when the request was made, and what context it was a part of. Collectively, these capabilities make it possible for each interplay between Visier and Amazon Fast, from a Fast chat agent question to a scheduled move, stays observable, auditable, and ruled.

Clear up

While you’re executed utilizing this integration, clear up the sources that you simply created:

1. Take away the MCP integration from Amazon Fast:
   1. From the Amazon Fast house display screen, navigate to Integrations within the left navigation panel.
   2. Choose the Actions tab, find the Visier MCP integration, and select Take away.
   3. This stops Visier information from being accessible by means of Amazon Fast.
2. Revoke Visier MCP credentials:
   1. Within the Visier admin console, navigate to Settings > API & Integrations.
   2. Revoke the MCP server credentials used for the Amazon Fast connection.
3. Take away Fast Areas content material (elective):
   1. In case you created Fast Areas particularly for this integration, navigate to Areas in Amazon Fast and delete them.
4. Delete the Amazon Fast surroundings (elective):
   1. In case you now not want the Amazon Fast surroundings, navigate to the AWS console and delete the related sources.
   2. This removes the related indexes, integrations, and information supply connectors.

Conclusion

The mixing of Visier and Amazon Fast by way of MCP demonstrates a sample that extends past individuals analytics to any state of affairs the place specialised enterprise intelligence should be grounded in organizational context.The worth isn’t in both system alone. Amazon Fast supplies the orchestration layer and enterprise context. Visier supplies the workforce intelligence. MCP supplies the safe, standardized connection between them. For the top person, the expertise is straightforward: ask a query, get a solution that pulls on every little thing the group is aware of, and act on it with out switching instruments.The identical structure applies throughout Finance, Operations, Gross sales, Advertising, and Authorized. Wherever workforce information and organizational context want to come back collectively, Amazon Fast and Visier, linked utilizing MCP, make that attainable in a single dialog.

Subsequent steps

Able to deliver workforce intelligence into your agentic AI workspace? Begin by visiting the Amazon Fast documentation to arrange your surroundings, configure integrations, and start constructing brokers and automations. For the Visier aspect, the Visier MCP Server documentation walks by means of setup directions, authentication configuration, and the complete set of accessible workforce analytics instruments.

To be taught extra about Visier’s Workforce AI platform, go to visier.com. For a deeper have a look at how Amazon Fast connects to exterior information sources by means of the Mannequin Context Protocol, learn Combine exterior instruments with Amazon Fast Brokers utilizing MCP.

Concerning the authors

Vishnu Elangovan

Vishnu Elangovan is a Worldwide Agentic AI Answer Architect with over a decade of expertise in Utilized AI/ML and Deep Studying. He loves constructing and tinkering with scalable AI/ML options and considers himself a lifelong learner. Vishnu is a trusted thought chief within the AI/ML group, repeatedly talking at main AI conferences and sharing his experience on Agentic AI at top-tier occasions.

Vipin Mohan

Vipin Mohan is a Principal Product Supervisor at Amazon Internet Providers, the place he leads Agentic AI product technique. He makes a speciality of constructing AI/ML merchandise, container platforms, and search applied sciences that serve hundreds of shoppers. Outdoors of labor, he mentors aspiring product managers, enjoys studying about monetary investing and entrepreneurship, and loves exploring the world by means of the eyes of his two youngsters.