---
title: "Agentia Project Management Agent Project"
source: Panaversity Learn Agentic AI
source_url: https://github.com/panaversity/learn-agentic-ai
licence: Apache-2.0
domain: agentic-ai
subdomain: panaversity-agentic
date_added: 2026-04-25
---

# Agentia Project Management Agent Project

Below is a suggested **seventh project** in your **Agentia** learning path. This time, you will create a **Team Collaboration and Project Management Agent**, leveraging multi-agent orchestration, LLM-based tool-calling, and user-centric design to manage tasks, deadlines, and team collaboration efficiently.

---

## Project Overview

1. **Goal**  
   - Build a **Team Collaboration and Project Management Agent** that helps users (and potentially an entire team) organize tasks, track progress, schedule meetings, and receive summaries of project updates.  
   - Use **LLM-based function-calling** to parse natural language commands (e.g., “Create a new sprint backlog item” or “Summarize yesterday’s stand-up meeting”), integrating with existing project management APIs (e.g., Jira, Trello, Asana).  
   - Maintain a **human-in-the-loop** approach for final approvals or major changes (e.g., reprioritizing tasks, shifting deadlines).

2. **Key Components**  
   1. **Front-End Orchestration Agent** (existing)  
      - Continues serving as the single entry point for user interactions.  
   2. **Greeting Agent** (existing)  
      - Handles trivial greetings, small talk.  
   3. **User Preference Agent** (existing)  
      - Stores user-specific or team-specific preferences: default task assignee, sprint lengths, preferred meeting times.  
   4. **Knowledge Graph Agent** (optional)  
      - Could store relationships among tasks, team members, sprints, or dependencies (e.g., “Task A depends on Task B being completed”).  
   5. **Mail Processing Agent** (optional)  
      - Can handle email notifications for task assignments, daily stand-up summaries, or backlog changes.  
   6. **Project Management Agent** **(New)**  
      - Integrates with popular PM tools (Jira, Trello, Asana) or a mock/standalone task system.  
      - Uses an **LLM** to interpret user commands and call “tools” such as `CreateTask`, `UpdateDeadline`, `GenerateDailySummary`, etc.  
      - Provides a **human-in-the-loop** step for any high-impact changes (e.g., reassigning tasks, adjusting sprint scope).

3. **Value Proposition**  
   - Illustrates how multi-agent systems and LLM-based orchestration streamline everyday team and project management tasks.  
   - Shows how **natural language** can facilitate complex operations in a standard PM tool, coupled with user oversight to maintain team alignment.

---

## 1. Plan the Architecture

1. **Service Layout**  
   - The **Project Management Agent** is a new service/container that:  
     - Communicates with a project management tool or API.  
     - Leverages LLM function-calling to interpret and execute user commands.  
   - Other agents continue to operate as separate services/containers (Front-End, Greeting, Preferences, etc.).

2. **Communication Patterns**  
   - The LLM in the **Project Management Agent** will have tool definitions such as:
     - **CreateTask**(title, description, assignee, due_date)  
     - **UpdateTask**(task_id, new_status, new_due_date)  
     - **GenerateDailySummary**(project_id)  
     - **RescheduleMeeting**(meeting_id, new_time)

3. **Human-in-the-Loop Approval**  
   - For risky or large-scope actions—like completely reorganizing a sprint or removing multiple tasks—the system can return a **draft** requiring user confirmation.  
   - The user can approve or modify these changes through the **Front-End Orchestration Agent**.

---

## 2. Project Management Agent

### 2.1 Responsibilities

1. **Parse Project Commands**  
   - e.g., “Create a new task for the UI redesign, assign it to Alice, deadline next Friday.”  
   - e.g., “Move task #123 to ‘In Progress.’”  
   - The LLM interprets the instruction and calls the correct tool (e.g., `CreateTask`, `UpdateTask`).

2. **Fetch and Summarize Project Data**  
   - e.g., “Show me all tasks assigned to me this week,” or “Give me a status update on Sprint 5.”  
   - The agent calls the PM tool’s APIs to retrieve data, then uses the LLM to generate a concise summary.

3. **Meeting and Calendar Coordination**  
   - If integrated with a calendar (Google Calendar, Outlook), the agent can find meeting slots or reschedule events.  
   - Returns a draft action for user approval if it involves changing multiple people’s schedules.

4. **Daily Stand-up or Weekly Report Generation**  
   - The agent can automatically compile stand-up notes or weekly summaries from task updates and user activity.  
   - Possibly uses the **Mail Processing Agent** to send the summary via email or the **Knowledge Graph Agent** to store cross-project relationships.


## 3. Front-End Orchestration Agent: Extended Logic

1. **Identify Project Management Requests**  
   - If the user’s input relates to tasks, sprints, backlogs, or status updates, route to the **Project Management Agent**.  
   - Other requests (greetings, personal finance, etc.) remain handled by their respective agents.

2. **Draft Confirmation**  
   - If the PM Agent returns a `draft: true`, ask the user whether to finalize or modify.  
   - On approval, the front-end calls `POST /project_management/finalize` or a similar endpoint.

3. **Fallback**  
   - For requests not recognized as project management tasks or if the PM Agent cannot parse the instruction, prompt the user for clarification.

---

## 4. Demonstration Scenario

1. **User**: “Hello!”  
   - **Front-End** → **Greeting Agent** → “Hi there! How can I help you today?”  
2. **User**: “Please create a new task: ‘Design login screen,’ assigned to Sarah, with a deadline next Monday.”  
   - **Front-End** → **Project Management Agent** → The agent calls `CreateTask` with the relevant details.  
   - Returns a draft: “Task ‘Design login screen’ for Sarah, due next Monday. Approve?”  
3. **User**: “Yes, approve.”  
   - **Front-End** → Finalizes with the PM Agent, which updates the project management tool (e.g., Jira or Trello).  
   - The agent confirms: “Task created successfully.”  
4. **User**: “What are Sarah’s tasks for this sprint?”  
   - **Front-End** → **Project Management Agent** → The agent calls a PM API to filter tasks assigned to Sarah in the current sprint.  
   - Summarizes: “Sarah has three tasks: ‘Design login screen,’ ‘Fix header CSS,’ and ‘Update user onboarding flow.’”  
5. **User**: “Move ‘Fix header CSS’ to In Progress.”  
   - **Front-End** → **Project Management Agent** → Calls `UpdateTask(task_id=X, new_status='In Progress')`.  
   - Returns a success message or a draft if needed.

---

## 5. Deployment and Testing

1. **Local or Cloud Setup**  
   - Containerize each agent (Front-End, PM Agent, etc.).  
   - Connect the PM Agent to a **sandbox** or **mock** project management API (or use a real account with test projects).

2. **LLM Function-Calling**  
   - Define clear “tools” for creating/updating tasks, retrieving sprint or backlog data, summarizing stand-up notes.  
   - Verify the LLM can parse user instructions and correctly call these tools with structured parameters.

3. **Integration Points**  
   - Optionally link a calendar system for meeting invites or sprint review scheduling.  
   - Optionally link with the **Mail Processing Agent** to send daily or weekly project summaries.

4. **Observability**  
   - **Project Management Agent**: Log each action (task creation, status updates, meeting scheduling).  
   - **Front-End**: Log the user’s final confirmation or rejections.

5. **Error Handling**  
   - If the PM tool’s API returns an error (e.g., invalid assignee), the agent should produce a friendly message for resolution.

---

## 6. Possible Enhancements

1. **Task Dependency Modeling**  
   - Use the **Knowledge Graph Agent** to store dependencies (Task A depends on Task B).  
   - The system can warn the user if they try to start a task before its prerequisite is finished.

2. **Intelligent Task Prioritization**  
   - Incorporate basic ML to suggest priorities based on deadlines, effort estimates, or historical data.  
   - The user can override or confirm these suggestions.

3. **Advanced Summaries and Reporting**  
   - Let the LLM generate daily or weekly “stand-up” style summaries from multiple tasks and statuses.  
   - Possibly include burn-down charts or velocity metrics if integrating with agile frameworks.

4. **Team Collaboration and Notifications**  
   - The system could post updates to Slack or Microsoft Teams channels automatically, or email stakeholders when major tasks are completed.

5. **Multi-Project Management**  
   - Extend the system to handle multiple projects, each with different teams or boards.  
   - The user can ask cross-project questions (“Show me tasks assigned to me across all active projects.”).

6. **Time Tracking Integration**  
   - Connect with time-tracking tools (e.g., Harvest or Toggl) to automatically log time spent on tasks and generate timesheet reports.

---

## Conclusion

This **seventh project** focuses on creating a **Team Collaboration and Project Management Agent**, demonstrating how **Agentia** can streamline:

- **Project management tasks** (creating, updating, summarizing)  
- **Team communication** (assigning tasks, scheduling sprints, updating statuses)  
- **Human-in-the-loop** workflows (confirming major changes, approvals)  
- **LLM-based orchestration** (interpret natural language commands, call PM tool APIs, generate reports)

By integrating with real or mock project management tools, you illustrate how **multi-agent systems** and **conversational AI** can dramatically improve productivity and clarity for teams operating in fast-paced or agile development environments.
