a16z Talks with Decagon: Engineering and Running Enterprise AI Agents at Scale
a16z · x · 2026-07-31
a16z hosted the founders of Decagon, an enterprise AI customer service company, to discuss deploying AI agents in major banks, airlines, and telcos.
Key Insights:
- Model Strategy: The smart-vs-cheap model trade-off is false. Start with frontier models for new use cases, then migrate to open-source as they mature (90% of Decagon's traffic runs on open-source).
- Fine-tuning: Fine-tuning per use case works effectively at the application layer.
- Business Logic: Support demand always outran supply. Making it cheaper means companies buy more (Jevons paradox).
- Transparency: Glass box beats black box.
- Future: The help desk will evolve into an AI concierge.
Related event: a16z x Decagon: Open-Source Migration and Moat in Enterprise AI Agents(6 posts)→
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