Agentic Search Integration

5 posts · Jul 2026 – Sep 2026

What this trail covers

This post details the development of a new search capability for Databricks AI agents, addressing limitations in inherited web search functionalities. It introduces techniques to improve search relevance and performance for agents, likely involving custom indexing, retrieval strategies, or integration with internal knowledge bases to overcome the shortcomings of generic web search. This aims to provide agents with more accurate and contextually relevant information for their tasks. The Data Onto

All Databricks trails

Sep 2026Deploy and manage coding agents at scale with the Unity Gateway CLIThis post introduces the Unity Gateway CLI, a new command-line interface for deploying and managing coding agents at scale. It addresses the operational challenges of managing multiple agents by providing centralized control, configuration, and deployment capabilities. The CLI allows for efficient provisioning, updating, and monitoring of agents, enabling teams to scale their AI development and deployment workflows more effectively. This builds upon the existing agentic search integration by providing the tooling to manage these agents in production.
Sep 2026The Web Search Your Agent Inherited Isn't Good EnoughThis post introduces a new capability for Databricks AI agents to overcome the limitations of inherited web search. It details the technical challenges of using generic web search for agents, highlighting issues with relevance, noise, and the need for structured information. The post describes the development of a custom search solution that likely involves techniques for filtering, ranking, and potentially re-ranking search results to better suit agentic needs. It also touches upon the trade-offs between using general web search and a more specialized, agent-aware search mechanism, emphasizing the need for agents to have access to high-quality, relevant information to perform their tasks effectively.
Sep 2026Data Ontology defined: The context layer your AI agents are missingThis post introduces the concept of a Data Ontology as a crucial context layer for AI agents, addressing the limitations of current search functionalities. It details how a structured ontology, defining entities, relationships, and semantics, can significantly improve an agent's ability to understand and retrieve relevant information. The post outlines the technical challenges and potential solutions for building and integrating such an ontology, emphasizing its role in enabling more sophisticated reasoning and task completion for AI agents. This builds upon the existing Agentic Search Integration capability by providing a foundational layer for more intelligent and context-aware search.
Aug 2026What are AI Hallucinations?This post defines AI hallucinations as a phenomenon where AI models generate plausible but factually incorrect or nonsensical information. It discusses the underlying causes, such as limitations in training data, model architecture, and the probabilistic nature of language generation. The post also touches upon strategies for mitigating hallucinations, including improved data curation, advanced model training techniques, and incorporating external knowledge sources. While not detailing a specific system build, it provides crucial context for understanding and addressing a core challenge in AI development and deployment.
Jul 2026Agentic media buying cannot scale without the right foundation. See how buyers and sellers get there on Databricks.This post details how Databricks enables agentic media buying by providing a foundational platform for buyers and sellers. It highlights the technical challenges and solutions for scaling agentic workflows in media buying, focusing on the underlying infrastructure and capabilities that support efficient and effective operations. The post emphasizes the importance of a robust foundation for agentic systems to achieve scalability and performance in complex domains like media buying.
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