How to Build an AI Call Agent with Python, Groq and TTS

Building an AI call agent with Python for automated outreach, such as trucking dispatch operations, relies on chaining four core components in a continuous loop: speech-to-text to process incoming caller audio, an LLM such as Groq to determine the appropriate response, text-to-speech using tools like Edge TTS to synthesize the reply, and call-state detection ensuring the agent speaks only after a real person answers. This architecture powers the Google Voice Dispatch Agent built specifically for logistics outreach.

The runtime execution flow begins by loading contacts and configuration settings, executing preflight checks, pre-generating opening lines and voicemail audio files, dialing the number, waiting for confirmed connection, running the main conversation loop, and finally saving logs before transitioning to the next contact after a mandatory cooldown period.

Stock photo for illustration only, not from the actual event

An agent speaking while the phone is still ringing ruins the interaction. To prevent this, the Dispatch Agent implements a conservative state gate: ringing cannot transition directly to ended, a minimum ring window must elapse before any state change is registered, and the opening line is triggered only after connection is verified.

For audio transcription and language generation, the agent leverages the Groq client with models like whisper-large-v3 for speech-to-text conversion. Chat models such as llama-3.1-8b-instant process the dialogue using a concise, job-specific system prompt focused on confirming availability, lanes, and equipment. Responses are restricted to one or two sentences to maintain conversational realism, with synthesized audio played into the call via a virtual audio device (VB-CABLE) set as the browser microphone.