What happens when AI agents need more than a prompt to get the right answer?
This report explores why context engineering is becoming critical to the next stage of AI. As agentic AI moves beyond simple chat interactions into workflows that reason, retrieve and act, the quality of the context supplied to large language models becomes increasingly important. Hybrid search offers a way to combine keyword precision, semantic understanding and reranking to deliver more relevant information to AI systems.
In this report, you’ll learn:
• Why prompt engineering alone is not enough for agentic AI
• How context engineering helps AI systems access more relevant information
• Where hybrid search improves retrieval across lexical, vector and semantic methods
• Why retrieval augmented generation depends on high-quality context
• How reranking can improve the relevance of results supplied to AI agents
• The role of Model Context Protocol in connecting agents with tools and data sources
• Why access controls and governance matter when AI agents retrieve private data
It also outlines how organisations can support more reliable AI workflows by improving the way agents find, filter and use context across enterprise data.
Access the full report to explore how hybrid search can help agentic AI systems deliver more accurate, relevant and useful responses.
