Agentic Search
About Topic
Search engines have evolved dramatically over the years. We began with simple keyword-based systems, which matched queries to documents through literal terms. Then came semantic search, a leap forward that interpreted intent and context to deliver more relevant results. Yet, despite these advancements, we’ve hit a ceiling: even the most sophisticated search engines today struggle to answer complex questions in a single query.
To break through this barrier, we need agentic search—a paradigm where the system doesn’t just retrieve answers but actively strategizes. For example, decomposing a query into sub-questions, iterating through hypotheses, or synthesizing insights across multiple sources. This approach doesn’t just tweak accuracy by a few percentage points; it redefines search.
Key Takeaways
- RAG accelerates software testing processes by automating test case generation, and knowledge retrieval, reducing manual effort.
- Improves decision-making in QA by providing AI-driven insights, helping teams quickly identify impacted test cases and prioritize testing efforts.
- Reduces costs and time-to-market by streamlining test design, execution, and defect analysis, allowing teams to focus on high-value tasks.
About Speaker
Florian has 10 years of experience in the Field of AI, working at Axel-Springer, Deloitte, and SoundCloud. He works as a Principal Engineer at Jina AI, rapidly prototyping AI solutions. His expert domain is Agentic Search and synthetic data generation. Florian is serving as an AI policy advisor, providing explanations and insights to members of the European Parliament to enhance their understanding of artificial intelligence.