⏭️ Forward Deployed: Voice AI on what works in 2026
In this Forward Deployed Engineering panel hosted by Basil Chatha, engineering leaders from Daily, Decagon, Vapi, Retell AI, and Smallest AI discuss the realities of building and deploying enterprise voice agents. The panel details why modular cascaded pipelines remain dominant over end-to-end speech-to-speech models, how engineering teams manage turn-taking and latency constraints, and the technical trade-offs between monolithic system prompts, specialized workflows, and self-hosted small language models.
- Basia Sudol points out that enterprise voice agents require supervisor models to block prompt injection and social engineering attacks before an answer speaks. Read →
- Outbound voice agents have asymmetric guardrail safety: in narrow tasks like debt collection, the agent can drop the call whenever a user veers off-script. Read →
- Engineering teams spend months shaving 10 milliseconds off individual pipeline steps, but third-party backend APIs often cause massive 5-second bottlenecks. Read →
- Large language models suffer from severe context degradation in voice calls, often retaining only the first 4% and last 4% of a prompt while ignoring instructions placed in the middle. Read →
- Global voice models tuned for US English and Spanish fail immediately in markets like Japan and Arabic-speaking countries due to localized phonetic rules. Read →
- OpenAI API latency spikes kill conversational voice agents, where delays over 500 milliseconds ruin human turn-taking. Read →