MLOps.community How Sierra AI Does Context Engineering
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Dec 10, 2025 
Zack Reneau-Wedeen, Head of Product at Sierra, shares insights on revolutionizing AI with context engineering, prioritizing real-world testing over traditional methods. He reveals how AI often feels like a moody coworker and discusses the importance of robust simulations to enhance reliability. Zack advocates for abandoning decision trees in favor of goal-oriented frameworks and explains how Sierra trains graduates to be product-engineering hybrids. He also emphasizes the significance of customer focus to improve AI agents and discusses innovative strategies for scaling and fine-tuning voice interactions.
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Rethink Testing For Non-Deterministic AI
- AI systems are slow, expensive, and non-deterministic compared to traditional software, so testing must change accordingly.
- Sierra runs many parallel simulations and uses LLM evaluators to judge conversational outcomes instead of single-run unit tests.
Simulate Real-World Voice Chaos
- Simulate realistic voice conditions by adding background noise, accents, and low-quality microphones to test robustness.
- Run hundreds of varied simulations multiple times to gain confidence before wide deployment.
Use Both Red-Teams And Verbatim Tests
- Combine adversarial (red-team) and verbatim scripted simulations to catch prompt-hijacks and abuse cases.
- Use deterministic verbatim tests for known malicious inputs that models struggle to imagine.
