AI Workflow Generation: Automating Administrative Pipelines End-to-End

Introduction

Modern administrative systems suffer from rigid workflows, manual intervention, and slow escalation cycles. Mudda solves this by introducing an AI-driven workflow generation engine that dynamically constructs execution plans in real time.

What Is AI Workflow Generation?

Large language models and policy engines transform the way unstructured issues are converted into structured, executable workflows. Instead of relying on hard-coded decision trees, our system analyzes the input to determine the optimal path forward.

Key components of this process include:

• Context Ingestion: Analyzing raw text, images, and metadata from the user report.
• Intent Extraction: Identifying the core problem (e.g., "pothole repair" vs. "traffic violation").
• Constraint Reasoning: Checking against local laws, budget constraints, and jurisdiction boundaries.
• Tool Selection: Picking the right microservices (e.g., dispatch systems, notification services) to handle the task.

System Architecture Overview

The Agentic AI Plan Generator acts as the central brain, coordinating between various infrastructure components:

• Spring Backend: Handles core business logic and API management.
• Workflow APIs: Manages the state and transition of generated workflows.
• AI Classifiers: Specialized models for hate speech detection, duplicate reporting, and computer vision analysis.
• Databases: PostgreSQL for transactional data and Qdrant for vector search and semantic retrieval.
• Storage: S3 for secure handling of media assets.

Step-by-Step Workflow Generation

The pipeline follows a strict sequence to ensure reliability:

1. Issue Ingestion: The user submits a flagged issue via the mobile or web app.
2. Signal Enrichment: AI models analyze images and text to add metadata (severity, location context).
3. Semantic Validation: The system checks for duplicate reports using vector embeddings to avoid redundant work.
4. Plan Generation: The Agentic AI constructs a directed acyclic graph (DAG) representing the resolution steps.
5. Orchestration: Workflow APIs assume control, executing the DAG nodes.
6. Execution & Monitoring: Real-time tracking of task completion with automated escalation if SLAs are breached.

Automatic Orchestration

Efficiency is achieved through automation at every level:

• Tasks are parallelized where possible (e.g., notifying authorities while simultaneously updating the public dashboard).
• Dependencies are resolved to ensure tasks happen in the correct order (e.g., verification must happen after repair).
• State transitions are handled automatically, moving issues from "Reported" to "In Progress" to "Resolved" without manual clicks.
• Human-in-the-loop checkpoints are inserted only when high-stakes decisions require human judgment.

Example Workflow: "Road Pothole Complaint"

Here is how the system handles a common civic issue:

1. Image Analysis: Computer vision confirms the image contains a pothole and estimates its size.
2. Duplicate Detection: The location is checked against active reports to merge duplicates.
3. Authority Assignment: The specific municipal department responsible for that street is identified.
4. SLA Tracking: A resolution deadline is set based on the severity (e.g., 48 hours for deep potholes).
5. Resolution Verification: After the fix is reported, the user or an inspector verifies the work before closing the ticket.

Why This Matters

For civic infrastructure, this shift is transformative:

• Reduced Admin Load: Automation handles the routine routing and tracking, freeing up staff for complex cases.
• Faster Resolution: Removing manual handoffs drastically cuts down the time from report to fix.
• Policy-Compliant: Every generated workflow is checked against the latest regulations, ensuring compliance.
• Scalable Governance: The system can handle thousands of concurrent issues without performance degradation.

Conclusion

Agentic workflow generation moves us closer to a self-healing administrative infrastructure. By treating civic issues as programmable workflows, Mudda enables cities to respond faster, smarter, and more efficiently than ever before.