Blog
Engineering deep dives, API design opinions, and industry perspectives from the team building Guava.
Guava and Bland AI take opposite approaches to voice agent infrastructure. Here's how they compare on setup complexity, latency, telephony, compliance, and total cost of ownership for engineering teams.
You stitched together Twilio, Whisper, and ElevenLabs. The demo impressed stakeholders. Then production traffic exposed every seam. Here's why orchestrated voice API chains collapse under real-world load.
The choice between orchestrating third-party APIs and owning the full stack creates cascading effects across latency, reliability, developer experience, and costs at production scale.
Most voice AI platforms ask you to write 500-word system prompts. This is a mistake. A good API is a contract — it should express intent precisely, not exhaustively. Guava's `set_task()` with a checklist is the antidote.
Every voice AI team we talk to is prioritizing the wrong thing. They optimize for naturalness when their bot still drops calls. They add features when latency is still 800ms. Maslow had the right idea.
No-code voice builders look impressive in demos. They collapse in production. You can't version-control a drag-and-drop flow. You can't test it. You can't compose it with your existing systems. Code is the right abstraction.
LLM-powered voice bots have a dirty secret: you never know exactly what they'll say next. For a demo, that's fine. For a regulated industry, it's a liability. Guava's checklist + `on_complete` architecture means you can reason about exactly what your agent will do.
The three checklist types — `Field`, `Say`, and plain Python strings — are deceptively simple. Together, they cover 90% of what any voice agent needs to do. Understanding when to use each one is the key to building bots that are both structured and natural.