[Suggestion] Voice agent behavioral testing harness with YAML-driven scenarios and automated quality scoring (Python)

[deepgram-robot]

What to build

A Python testing framework that lets developers define voice agent test scenarios in YAML, run them against Deepgram's Voice Agent API, and evaluate results using automated quality scoring — enabling CI/CD testing of voice agent behavior.

Why this matters

Developers deploying voice agents to production need automated testing to catch regressions — verifying that the agent greets correctly, handles edge cases, uses tools appropriately, and stays on-topic. Currently, testing voice agents requires manual conversation, which doesn't scale and can't be integrated into CI/CD pipelines. A YAML-driven test harness that simulates user turns (as text or audio), captures agent responses, and evaluates them against expected behaviors would be the foundation for production-grade voice agent development. This is becoming standard practice in the voice AI ecosystem and a key retention lever for the platform.

Suggested scope

• Language: Python
• Deepgram APIs: Voice Agent API
• Key features:
  • YAML scenario definitions with user turns, expected agent behaviors, and success criteria
  • Text-mode testing (inject text as simulated STT) for fast iteration
  • Audio-mode testing (play audio files through the Voice Agent) for end-to-end validation
  • Automated scoring: LLM-as-judge evaluation of agent responses against criteria
  • Latency assertions: fail if time-to-first-response exceeds threshold
  • Function calling validation: verify correct tools were called with expected arguments
  • JUnit XML output for CI/CD integration
  • CLI runner: python -m dg_eval --scenarios ./tests/
• Complexity: Medium-high — testing framework with LLM evaluation

Acceptance criteria

• Runnable with minimal setup (clone, add API key, run)
• README explains the YAML scenario format and evaluation methodology
• Uses current SDK version
• At least 5 example scenarios included (greeting, FAQ, tool use, edge case, off-topic)
• Text-mode testing works without audio hardware
• Produces pass/fail results with detailed scoring per scenario
• JUnit XML output compatible with GitHub Actions / CI systems
• Latency thresholds configurable per scenario

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Raised by the DX intelligence system.