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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Quality shouldn't stop at the handoff
When an AI agent hands a call to a person, most QA splits one customer experience into two separate verdicts. Here’s how evaluating AI and human conversations together closes that gap, and how Parloa Lens brings both into one view.
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Parloa’s blueprint for reliability in production
Reliable AI in healthcare starts with a strong foundation. Building Parloa’s integration with Epic taught us four pillars we now rely on to keep AI agents secure, predictable, and accountable in production.
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How Parloa benchmarks models for enterprise AI agents
A higher overall score can hide a model’s failures in critical customer workflows. Learn more about how Parloa benchmarks models across individual use cases and turns the findings into practical guidance for builders.
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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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