Multi-agent architecture: A look inside Parloa’s Subtask Agents
Most multi-agent work leverages supervisor LLMs at the routing layer. Multi-agent work for Voice AI requires an alternative approach. Learn how the architecture differs.
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Engineering perspectives
What our team is thinking about before it becomes code, research, or product.
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Beyond Authorization: Ensuring Reliable Service when Personal AI Agents are your Customers
A customer’s personal AI agent can get through your door, but it doesn’t know your refund policy, your API sequence, or your risk limits. Parloa's Personal Agent Middleware lets you enforce the same rules on every request, whether a human or an AI agent starts it.
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Using affordance to design intuitive AI agents
Human-sounding AI agents can raise expectations they aren’t designed to meet. See how affordances and signifiers make voice interactions clearer, easier to navigate, and easier to recover when things go wrong.
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Jev could be the missing decision layer for agentic CX
Large language models taught AI agents how to talk. System One models like Jev could teach them when to route, escalate, and act, and whether they succeeded.
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Building Parloa Zones: Repeatable regional deployments with central control
Adding a new region to Parloa used to take coordinated work across engineering teams. Learn how we built Parloa Zones, which lets customers run AI agents in the region they choose, so that standing up a new region is now largely automated.
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The A2B service layer: Why enterprises need one governed interface for personal agents
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What does it mean for a speech-to-speech model to understand a caller?
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