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Are pre-defined AI agents really the answer? Building accurate task-specific agents on the fly!
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No matter how many agents you build and how efficiently you orchestrate them, you can never cover all possible user queries and task scenarios with 100% accuracy.
In this session, we’ll explore the challenges of building rigid AI agents on enterprise data. We’ll focus on handling flexible user queries across siloed, heterogeneous data sets, including unstructured, structured, and vectorized data, as well as Saas applications, internal APIs, and external websites.
We’ll explore how traditional in-context pipelines often deliver inconsistent results – missing steps, losing context, hitting token-size limits, or generating varying answers – while our structured on-the-fly agents built with PromptQL separates query planning from execution to ensure accurate and reproducible outcomes.
Join us to see how this method delivers transparency, accuracy, steerability, and security. PromptQL empowers your AI to answer free-form queries across multiple data sources reliably, reducing the cost of mistakes while driving operational efficiency and business growth.