Building Kalisa: Challenges in AI Agent Development

Developing Kalisa, our AI agent platform, has been a journey filled with technical challenges, learning opportunities, and breakthrough moments. This post shares some of the key lessons we've learned while building AI agents using Retrieval-Augmented Generation and LangChain.

Balancing Context and Performance

One of the primary challenges in RAG systems is finding the optimal balance between context window size and performance. Including too much retrieved information can overwhelm the model and slow response times, while insufficient context leads to less informed responses.

Knowledge Management

Creating an effective knowledge base requires careful consideration of document structure, chunking strategies, and embedding approaches. We experimented with various text splitting techniques and found that semantic chunking often outperforms simple character or token-based splitting.

Scaling Considerations

As our user base grew, we faced challenges related to scaling our vector database and managing computational resources efficiently. Using HTMX for the frontend and optimizing our Flask backend helped us maintain responsiveness even under increasing load.

Throughout this process, we've maintained a focus on creating AI agents that provide genuinely helpful, accurate, and contextually appropriate responses. The technical challenges, while significant, have ultimately helped us build a more robust and effective platform.