Case Study
Case Study
Multi-Agent AI-Assisted Workflow for Frontline Managers
Multi-Agent AI-Assisted Workflow for Frontline Managers
Case Study
Multi-Agent AI-Assisted Workflow for Frontline Managers
Role
UX Design Intern
Role
UX Design Intern
Tools
Figma Miro Notion
Tools
Figma Miro Notion
Timeline
October'24-November'24
Timeline
October'24-November'24
Overview
Frontline managers handle a variety of employee queries daily. With AI becoming an integral part of workplace tools, there’s growing need for workflows where AI can automate routine queries while managers retain oversight for sensitive or complex ones.
This project explored how multi-agent AI systems could be designed to support managers while ensuring they remained empowered decision-makers
Overview
Frontline managers handle a variety of employee queries daily. With AI becoming an integral part of workplace tools, there’s growing need for workflows where AI can automate routine queries while managers retain oversight for sensitive or complex ones.
This project explored how multi-agent AI systems could be designed to support managers while ensuring they remained empowered decision-makers
What is the project about?
The goal was to design a platform where frontline managers could seamlessly manage employee requests in partnership with AI agents. The system needed to:
Automate repetitive/routine requests.
Provide managers with visibility and authority over AI outputs.
What is the project about?
The goal was to design a platform where frontline managers could seamlessly manage employee requests in partnership with AI agents. The system needed to:
Automate repetitive/routine requests.
Provide managers with visibility and authority over AI outputs.
Challenges Tackled
01︱Human-in-the-Loop Workflow
AI often struggles with complex or ambiguous requests, necessitating human intervention.
Solution: Designed a workflow where
AI agents handle routine queries and flag complex cases.
Managers receive clear visual cues for unresolved requests.
Managers can edit, approve, or completely rewrite responses manually or with the help of agents.
02︱Inline Edit for AI-Suggested Responses
Managers need to have the ability to tailor AI-generated responses to meet specific requirements such as tone, brevity, or factual accuracy.
Solution: Introduced an inline edit feature that allows managers to
Directly modify AI-suggested responses for clarity or precision.
Shorten responses to align with the tone of the conversation.
Quickly change the tone (e.g., professional, empathetic, or casual) for appropriate communication.
03︱AI Assistant
Managers needed a comprehensive tool to quickly understand issues and ask contextual questions about ongoing conversations between employees and AI agents.
Solution: Designed an AI assistant that
Summarizes conversations to provide concise overviews.
Allows managers to ask detailed, context-specific questions about the issue.
Provides accurate, fact-based answers with citations in collaboration with the agent responsible for the query.
Enables managers to make informed decisions without needing to sift through large amounts of data.
Challenges Tackled
01︱Human-in-the-Loop Workflow
AI often struggles with complex or ambiguous requests, necessitating human intervention.
Solution: Designed a workflow where
AI agents handle routine queries and flag complex cases.
Managers receive clear visual cues for unresolved requests.
Managers can edit, approve, or completely rewrite responses manually or with the help of agents.
02︱Inline Edit for AI-Suggested Responses
Managers need to have the ability to tailor AI-generated responses to meet specific requirements such as tone, brevity, or factual accuracy.
Solution: Introduced an inline edit feature that allows managers to
Directly modify AI-suggested responses for clarity or precision.
Shorten responses to align with the tone of the conversation.
Quickly change the tone (e.g., professional, empathetic, or casual) for appropriate communication.
03︱AI Assistant
Managers needed a comprehensive tool to quickly understand issues and ask contextual questions about ongoing conversations between employees and AI agents.
Solution: Designed an AI assistant that
Summarizes conversations to provide concise overviews.
Allows managers to ask detailed, context-specific questions about the issue.
Provides accurate, fact-based answers with citations in collaboration with the agent responsible for the query.
Enables managers to make informed decisions without needing to sift through large amounts of data.
Design Approach
Research
Our design process began with extensive stakeholder research:
Conducted in-depth interviews with frontline managers
Analyzed workflows in factory and HR management settings
Studied existing AI collaboration interfaces like ChatGPT's Canvas and Claude's Artifact feature
Gathered inspiration from tools like NotebookLM
Understanding the underlying tech
Retrieval-Augmented Generation (RAG) Integration: Retrieves precise information from company databases for context-aware responses.
The platform leverages RAG to:
Pull real-time, contextually relevant information from internal databases
Ensure accuracy and up-to-date responses
Provide comprehensive context for complex queries
Design Approach
Research
Our design process began with extensive stakeholder research:
Conducted in-depth interviews with frontline managers
Analyzed workflows in factory and HR management settings
Studied existing AI collaboration interfaces like ChatGPT's Canvas and Claude's Artifact feature
Gathered inspiration from tools like NotebookLM
Understanding the underlying tech
Retrieval-Augmented Generation (RAG) Integration: Retrieves precise information from company databases for context-aware responses.
The platform leverages RAG to:
Pull real-time, contextually relevant information from internal databases
Ensure accuracy and up-to-date responses
Provide comprehensive context for complex queries
Iterations
We drew our initial design inspiration from Claude and ChatGPT Canvas so early designs focused too heavily on automation, with managers stepping in only after failure. Later iterations re-framed the workflow around empowered oversight.
Added side panels with AI rationale to support decision-making.
Iteratively tested interaction points to ensure managers felt in control, not bypassed.
Iterations
We drew our initial design inspiration from Claude and ChatGPT Canvas so early designs focused too heavily on automation, with managers stepping in only after failure. Later iterations re-framed the workflow around empowered oversight.
Added side panels with AI rationale to support decision-making.
Iteratively tested interaction points to ensure managers felt in control, not bypassed.
Outcomes and Potential Impact
Reduced response times for employee queries
Increased manager efficiency
Improved workplace communication
Enhanced AI-human collaboration
Outcomes and Potential Impact
Reduced response times for employee queries
Increased manager efficiency
Improved workplace communication
Enhanced AI-human collaboration
Reflections
This project taught me the importance of designing systems that balance automation with human expertise. Key learnings include:
Human-AI Synergy: Trust in AI tools is built by keeping humans in control during critical moments.
Designing for Flexibility: Features like the AI assistant and inline editing allow users to adapt AI-generated outputs to specific needs.
Iterative Collaboration: Continuous feedback loops from users and engineers refined the design for real-world scenarios.
By centering managers as the human-in-the-loop, this project highlights how AI can augment human capabilities while ensuring critical decisions remain in human hands.
Reflections
This project taught me the importance of designing systems that balance automation with human expertise. Key learnings include:
Human-AI Synergy: Trust in AI tools is built by keeping humans in control during critical moments.
Designing for Flexibility: Features like the AI assistant and inline editing allow users to adapt AI-generated outputs to specific needs.
Iterative Collaboration: Continuous feedback loops from users and engineers refined the design for real-world scenarios.
By centering managers as the human-in-the-loop, this project highlights how AI can augment human capabilities while ensuring critical decisions remain in human hands.
Contact
Let's start creating together
Copy Email
Copied
Contact
Let's start creating together
Copy Email
Copied
Contact
Let's start creating together
Copy Email
Copied
Contact
Let's start creating together
Copy Email
Copied