project.md

Step-by-step explanation of how to accomplish the Vehicle Diagnostics Agent project end-to-end:

Vehicle Diagnostics Agent Project: Detailed Implementation Plan

Phase 1: Project Setup and Planning

1. Define Project Goals and Scope

   • Build a multi-agent AI system for predictive vehicle diagnostics.

   • Agents will collaboratively analyze sensor data to detect anomalies, identify causes, recommend maintenance, and generate reports.

   • Use realistic automotive sensor data (real/simulated).

   • Demonstrate production-readiness with FastAPI backend and Gradio interface.

2. Select Tools and Frameworks

   • LangChain and LangGraph for multi-agent orchestration.

   • Python for logic implementation.

   • PyTorch/TensorFlow for any ML model development.

   • FastAPI for service endpoints.

   • Gradio for user-friendly interface.

   • Docker for containerization.

3. Gather Data

   • Use open datasets like NASA Prognostics repository, Udacity self-driving car datasets, OR simulate vehicle telemetry in CARLA and inject anomalies.

Phase 2: Data Collection and Preprocessing

1. Acquire Vehicle Sensor Data

   • Collect time-series data such as engine temperature, speed, RPM, battery voltage, brake status, etc.

   • For supervised learning, acquire or generate corresponding anomaly/fault labels.

2. Clean and Process Data

   • Implement filtering to reduce noise (e.g., moving average, Kalman filtering).

   • Normalize and synchronize sensor streams.

   • Extract meaningful statistical and domain-specific features.

3. Split Data

   • Partition into training, validation, and testing datasets.

Phase 3: Build Individual Agents

1. Data Ingestion Agent

   • Load or stream sensor data into the system.

   • Prepare data for downstream agents.

2. Anomaly Detection Agent

   • Train and deploy ML models (e.g., LSTM, CNN) to detect unusual sensor patterns.

   • Use thresholding or probabilistic models for anomaly scoring.

3. Root Cause Analysis Agent

   • Implement rule-based or ML models to infer possible causes of anomalies by correlating sensor data patterns.

   • Integrate domain knowledge (e.g., engine fault codes mapping).

4. Maintenance Recommendation Agent

   • Map root causes to actionable maintenance steps or alerts.

   • Prioritize actions based on severity and impact.

5. Report Generation Agent

   • Compile diagnostic summaries into clear reports for users/operators.

   • Generate natural-language summaries.

Phase 4: Agent Orchestration and Workflow

1. Design Communication Protocol

   • Define how agents exchange information (inputs/outputs).

   • Implement context/memory sharing to maintain state across steps.

2. Implement Multi-Agent Orchestration

   • Use LangChain to manage sequential and parallel task execution among agents.

   • Define orchestration logic to call agents in order (Data Ingestion → Anomaly Detection → Root Cause → Recommendation → Report).

3. Add Error Handling and Recovery

   • Establish retry/fallback rules in case of agent failures or inconsistent data.

Phase 5: Backend and Frontend Development

1. FastAPI Service

   • Develop API endpoints for triggering diagnostics, retrieving reports, and health checks.

   • Handle concurrent user requests.

2. Gradio-based UI

   • Build an interactive dashboard for users to input vehicle IDs and view diagnostic reports.

   • Visualize detected anomalies and recommended actions.

Phase 6: Deployment and Monitoring

1. Containerization

   • Create Docker images for backend and frontend.

   • Use Docker Compose for service orchestration.

2. Deployment

   • Deploy locally or on cloud (AWS, Azure).

   • Configure environment variables and API keys securely.

3. Observability

   • Add logging and monitoring for system performance and errors.

   • Use LangSmith or other tracing tools to instrument agent workflows.

Phase 7: Testing and Validation

1. Unit Testing

   • Write tests for each agent’s logic.

   • Validate correct anomaly detection and recommendations.

2. Integration Testing

   • Verify multi-agent orchestration flows end-to-end.

   • Simulate vehicle scenarios including anomalies.

3. User Acceptance Testing

   • Gather feedback on Gradio interface usability and report clarity.

Phase 8: Documentation and Presentation

1. Write Comprehensive README

   • Explain project goals, architecture, how to run and extend.

   • Include example data and system diagram.

2. Prepare Demo and Presentation

   • Showcase live diagnostics on sample data.

   • Highlight modular design and agent collaboration.

Tasks to accomplish

| 1 | Data collection, preprocessing, build Data Ingestion & Anomaly Agents |

| 2 | Build Root Cause, Recommendation, Report Agents; implement LangChain orchestration |

| 3 | Backend (FastAPI), Frontend (Gradio), Deployment, Testing, Documentation |

• Multi-agent AI system design and orchestration

• Production-grade ML pipeline development

• Cross-functional, safety-critical domain knowledge

• Full-stack deployment and user interface

• Strong data engineering and AI validation skills

This project will serve as a flagship portfolio piece so one can apply AI to automotive challenges with agentic AI thinking.