I became interested in AI agents while building projects and realizing AI could do more than answer questions. I wanted agents to understand tasks, use tools, interact with APIs, and complete real-world workflows. What matters to me is making them reliable in production, not just getting them to work in a demo. My AI Voice Agent project put these ideas into practice; I describe it on linkedin profile.
- I evaluate tools by whether they solve a real problem and fit my current projects. I consider practical use cases, documentation, community support, and reliability. I prefer trying tools hands-on with a small project rather than following every trend, and invest more time only when I see real value.
- For voice-first AI, I start simply and get the complete conversation flow working: speech-to-text, the LLM, and text-to-speech together. Then I address latency, interruptions, and error handling. I aim for an experience that feels natural and useful, not merely one that can talk.
I consider communication and collaboration essential to scaling AI systems. Understanding business requirements, working across teams, and explaining technical decisions clearly help me ensure a solution addresses the right problem and works reliably for everyone.
- When debugging a voice pipeline, I check timing and integration between components. If turn detection triggers too early, the agent may respond before the user finishes speaking; components working individually do not guarantee a sound integration.
- I do not assume RAG completely eliminates hallucinations. Retrieved context can be irrelevant or incomplete, so retrieval quality and proper evaluation matter as much as the LLM itself.
- I prototype quickly to validate ideas. When something proves useful, I make it production-ready by improving code structure and adding testing, error handling, logging, and documentation. This lets me move quickly without sacrificing reliability or maintainability.
- I prefer building many small prototypes to explore ideas, learn what works, and fail fast; once I find something genuinely useful, I invest in making it reliable, scalable, and production-ready.