Real-time AI agents work directly inside live products. We design them around your processes so they respond instantly, behave predictably in production, and support real business workflows.
LiveKit Agents enable AI to operate directly within real-time audio and video streams. Instead of working with delayed inputs or static data, agents can interact with users live, respond in real time, and follow conversations as they happen. This approach is especially relevant for products where speed, timing, and natural interaction matter.
Live interaction changes how users experience AI. With this technology, companies can:
LiveKit Agents are often used where traditional chatbots or async AI tools fall short.
We see LiveKit Agents working best in products built around live interaction. This includes customer support during live sessions, voice assistants embedded in applications, internal tools that guide users in real time, and platforms where AI needs to react immediately to what is happening on screen or in a call. The exact setup depends on the product, the data available, and how much control is required over the agent’s behavior.
If you want a deeper technical and practical view, we’ve shared our experience in the article - LiveKit Agents: An architectural breakdown of the framework for building real-time AI agents.
We have been working with real-time communication and LiveKit-based systems for years as long-term partners. The platform’s recent $100M Series C funding further strengthens the foundation we rely on when building and scaling real-time AI solutions for production use. Our experience comes from developing products where reliability and predictable behavior are critical.
We don’t treat LiveKit Agents as a standalone feature. We design them as part of a complete solution, aligned with backend systems, business logic, and user flows. This helps teams avoid unstable setups and move toward solutions they can trust long term.
If you are considering real-time AI agents for your platform, we can help you design the right architecture and plan a safe path to production. Let’s talk about how this technology can support your product and business goals.
Testing usually starts in controlled environments using real audio and video flows. Teams focus on response timing, stability under load, and predictable behavior during live interaction before moving toward production.
They make sense for products built around live interaction, where AI is involved directly in ongoing sessions. This often applies to live support, guided workflows, and real-time use cases where timing, context, and consistent behavior are essential.
Jakub Bílý
Head of Business Development
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