Problem Statement

EV owners face fragmented information about charging stations, range anxiety, and complex maintenance schedules. No single intelligent assistant existed that could handle multi-turn conversations while accessing real-time data.

Solution

Built an agentic AI system using LangGraph that orchestrates multiple specialized agents — a charging station finder, route optimizer, and maintenance advisor — unified through a conversational interface with RAG for domain-specific knowledge retrieval.

Key Features

• ▸Multi-agent orchestration with LangGraph state machines
• ▸RAG-powered knowledge base for EV-specific information
• ▸Real-time charging station recommendations
• ▸Conversational route planning with range optimization
• ▸Maintenance schedule tracking and alerts
• ▸Natural language interface via Streamlit

Challenges

• ⚡Designing reliable state transitions between multiple agents
• ⚡Balancing retrieval accuracy with response latency
• ⚡Handling ambiguous user queries across different domains
• ⚡Implementing graceful fallbacks when APIs are unavailable

Results & Metrics

Lessons Learned

• 💡LangGraph state machines provide excellent control over complex agent workflows
• 💡RAG retrieval quality depends heavily on chunking strategy and embedding model selection
• 💡Designing clear agent boundaries is crucial for reliable multi-agent systems