Here are some friendly tips for keeping your agents running smoothly across different use cases in LiveKit: • Use Multi-Agent Workflows: When things get a bit complex, try setting up agent handoffs to make sure conversations go to the right place. Sharing state between agents helps keep the chat flowing naturally and lets you reuse chat history when switching agents. • Secure API Keys and Secrets: Keep your sensitive info safe by storing credentials in environment variables. It’s best to stick to security basics like using the least permissions needed and setting token expirations. • Leverage Built-in Chat Context: The

ChatContext

is your friend for managing conversation history. Defining tools with clear parameter schemas also helps keep things running smoothly and reliably. • Implement Robust Error Handling: Life happens—so be ready for things like API call hiccups, connection drops, and timeouts. Adding retry logic and graceful error handling can save the day. • Optimize for Deployment and Scaling: Use environment variables for easy configuration, set up autoscaling, and consider deploying agents on secure platforms like LiveKit Cloud for peace of mind. • Test Agents Thoroughly: Give your agents a good workout with automated tests and built-in frameworks to make sure they behave just right. • Tailor Agent Behavior: Customize your agents to fit your specific needs by choosing the right LLM models, tweaking temperature settings, and adding domain-specific instructions. Following these tips will help your agents stay secure, reliable, and ready for anything—from voice assistants to enterprise workflows. If you want to dive deeper into any of these, just let me know—I’m here to help! Sources: complex-agents/medical_office_triage/triage.py | LLM Integrations | livekit/livekit_composite | DeepWiki | LiveKit CLI | livekit/livekit_composite | DeepWiki | examples/README.md | main.py