You are on a voice call with an AI customer support agent. You ask a highly technical security question. Instead of the agent hallucinating, it says, "Let me patch in our security engineer," and the voice changes dynamically. Welcome to Multi-Agent Orchestration.
The Problem with the Mega-Prompt
Attempting to give one single LLM instructions on how to handle billing, technical support, sales objections, and polite small talk results in a massive prompt that slows down inference speed (latency) and increases hallucinations. A localized context window is far more accurate.
The Multi-Agent Router
In a multi-agent architecture, the WebRTC session connects to an initial "Router Agent." This agent uses a fast, cheap model (like GPT-4o-mini) with a single instruction: determine the intent of the user.
Once the intent is classified (e.g., "Billing Inquiry"), the orchestration engine instantly swaps the active LLM context and the TTS voice to a specialized "Billing Agent" model. The user perceives this as being "transferred" to another department.
Engineering the Handoff
This handoff must occur with zero latency drop for the user. Frameworks like Pipecat handle this by maintaining a persistent shared context object. The Billing Agent instantly inherits the transcript of the conversation the Router Agent had so far, preventing the user from having to repeat themselves. This modular architecture allows massive enterprise teams to build separate, specialized AI agents and orchestrate them elegantly into a single phone call experience.
Deploy Complex Agent Systems
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Design Multi-Agent Workflows