Workstatus – AI Knowledge & Task Automation Agent
Agentra’s AI Agent automated Workstatus’ knowledge retrieval and team task scheduling, cutting query times from hours to seconds and boosting productivity by 45%.
Executive Summary
Client
Industry
AI Agent Deployed
Channels Used
Problem Context
The Challenge:Workstatus, a workforce productivity SaaS platform, was struggling with internal operational efficiency. Product teams, customer success, and support often had to dig through scattered Confluence docs, Jira tickets, and internal chat threads to find answers or schedule cross-team actions.
Key Issues Faced:- Manual searching for product updates & release notes caused delays in responding to customer queries.
- High dependency on senior team members to clarify workflows and technical steps.
- Meeting and task scheduling across distributed teams required multiple follow-ups.
Solution: Agentra AI Agent Deployed
- Agent Type
- Engage + Schedule hybrid (Knowledge Retrieval & Task Scheduling)
- Use Case Handled
- End-to-end product knowledge retrieval, quick escalation routing, and automated cross-team meeting scheduling.
- Interaction Channels
- Text, voice, and Slack/MS Teams integration
How It Works
Input
Users ask questions or request scheduling via chat/voice.
LLM Understanding
Agentra identifies intent and fetches relevant product knowledge or schedules meetings.
Data Retrieval
Uses RAG with Workstatus, and product release notes database.
Action
Integrates with Google Calendar to assign tasks or book slots.
Escalation
Only routes to human support when no matching answer or action is found.
Agent Architecture View (Agentic Workflow)
Architecture Diagram Components:- LLM: GPT-4 + LangChain for reasoning
- Memory: Stores user preferences and past queries
- RAG: Workstatus + Release Notes database for context
- Tools: Google Calendar API, Workstatus API for scheduling
- Planner: Multi-step orchestration for complex tasks
Business Impact & Outcomes
Measured Improvements:- Query resolution time: 10–15 mins → 30 seconds
- Manual scheduling effort reduced by 70%
- Internal meeting coordination efficiency improved by 2.3x
Key Features Showcased
Measured Improvements:- Multi-modal AI: Text, voice, and Slack/MS Teams integration
- Agentic Tool Use: Automated Workstatus ticket creation & meeting scheduling
- LLM Reasoning: Context-aware answers to complex, multi-turn questions
- Autonomy: Self-completes most tasks without human intervention
- Memory & Personalization: Remembers recurring team meeting times and preferred escalation paths
Agent in Action (Workflow)
Workflow Snapshot:- User: “What’s the status of the new time-tracking API rollout?”
- Agent: Retrieves release notes + Workstatus tickets, responds in seconds.
- User: “Schedule a sync with the backend team this Friday.”
- Agent: Finds available slots, sends invites, confirms in chat.
- Follow-Up: Summarizes meeting notes after session (if integrated with transcription tool).
Why Agentra?
Agentra stood out due to:- Pre-built agents-ready templates for quick deployment.
- Multi-channel engagement without separate integrations.
- Strong API & EMR connectivity.
- Agentic workflows allowing autonomous, multi-step ticket handling.
“Before Agentra, our teams wasted hours hunting for answers and coordinating meetings. Now, AI handles 80% of it — freeing us up to build and ship faster.”
Ready to Transform Your SaaS Team?
Discover how AI agents can boost efficiency like they did for Workstatus.