Agentic AI

Voice agents are easy to demo and hard to scale. The architecture we ended up with fires every possible response in parallel and lets a fast router pick the winner mid-flight. This post walks through everything we tried before it, and why this design is the one we kept. The problem nobody’s demo solves Every voice agent demo looks the same: clean audio, a cooperative user, a short scripted exchange. Then you ship it, and two things break immediately. ...

Voice agent demos are easy to build. A LiveKit worker, an STT → LLM → TTS pipeline, and you have something that talks. The hard part is making it reliable. We run AI call agents on LiveKit. An call lasts up to an hour. A lost recording is a compliance problem. A lost transcript means the user has to redo the call. A silent agent means a human sitting in an empty room, wondering if anyone is there. ...

The Real Challenge in Building AI Agents Isn’t the AI - It’s Everything Else The AI industry has a marketing problem. We’ve become so infatuated with the “intelligence” in artificial intelligence that we’ve forgotten the most important truth about building agentic AI systems: 90% of the work is software engineering, and only 10% is actually about the AI model itself. This isn’t just a hot take - it’s a hard-learned lesson from intense 11 months of building AI interview and agentic systems at Eightfold AI, where this realisation became both the problem statement and a career pivot. While everyone’s debating which foundation model has the highest benchmark scores, the real battles are being fought in error handling, state management, and API integrations. ...