For more than a decade, conversational AI has promised human-like assistants that can do more than chat. Yet even as large language models (LLMs) like ChatGPT, Gemini, and Claude learn to reason, explain, and code, one critical category of interaction remains largely unsolved — reliably completing tasks for people outside of chat. Even the best AI models score only in the 30th percentile on Terminal-Bench Hard, a third-party benchmark designed to evaluate the performance of AI agents on completing a variety of browser-based tasks, far below the reliability demanded by most enterprises and users. And task-specific benchmarks like TAU-Bench airline, which measures the reliability of AI agents on finding and booking flights on behalf of a user, also don’t have much higher pass rates, with only 56% for the top performing agents and models (Claude 3.7 Sonnet) — meaning the agent fails nearly half the time. New York City-based Augmented Intelligence (AUI) Inc., co-founded by Ohad Elhelo and Ori Cohen, believes it has finally come with a solution to boost AI agent reliability to a level where most enterprises can trust they will do as instructed, reliably. The company’s new foundation model, called Apollo-1 — which remains in preview with early testers now but is close to an impending general release — is built on a principle it calls stateful neuro-symbolic reasoning.It’s a hybrid architecture championed by even LLM skeptics like Gary Marcus, designed to guarantee consistent, policy-compliant outcomes in every customer interaction.“Conversational AI is essentially two halves,” said Elhelo in a recent interview with VentureBeat. “The first half — open-ended dialogue — is handled beautifully by LLMs. They’re designed for creative or exploratory use cases. The other half is task-oriented dialogue, where there’s always a specific goal behind the conversation. That half has remained unsolved because it requires certainty.”AUI defines certainty as the difference between an agent that “probably” …
