Why AI Agents Need a Stronger Knowledge Layer to Reach Production
Based on a survey of 300 data, AI, and technology professionals, this report examines how organizations are building the knowledge capabilities that AI agents need to operate effectively.
The report has three objectives. First, it assesses an organization’s agent knowledge capabilities—the ability to give AI agents a complete contextual understanding of the data they ingest. This includes semantic knowledge, episodic memory, and procedural knowledge.
Second, it explores the challenges organizations face when improving access to knowledge and moving more AI agent use cases into production. Third, it considers the steps organizations are taking to overcome those challenges.
Key findings from the AI agent knowledge report
Weak data and knowledge capabilities slow AI agent progress
On average, only about one-third (34%) of an organization’s AI agent projects reach production. Even technology companies face this challenge. Legacy data systems, security and privacy concerns, and insufficient knowledge and context are among the key failure points.
Stronger knowledge capabilities correlate with AI agent success
A small group of production leaders—organizations where an average of 61% of agent projects progress beyond the pilot stage—report stronger knowledge capabilities than other groups, particularly in semantic knowledge. This advantage is closely related to higher production rates.
Fragmented data makes knowledge access more difficult
Data fragmentation, including inappropriate data sharing between systems, was the most commonly cited challenge to expanding AI agents’ access to knowledge. It was identified by 55% of respondents. By comparison, production leaders were more likely to view security and privacy as a primary concern, with 72% of this group identifying it as an issue.
Organizations are strengthening the connection between data and AI agents
Executives expect that strengthening the structural foundation connecting organizational data with AI agents will have the greatest impact on agents’ decision-making. The experts interviewed for the report identified the knowledge base as the primary way to build this connection.
Investment priorities include data pipelines, RAG, and knowledge graphs
To expand AI agents’ access to knowledge, organizations are prioritizing investments in search and knowledge technologies, including ingestion pipelines, AI-enabled APIs, retrieval-augmented generation (RAG), AI agent evaluation, and knowledge graphs.
Download the full report on the AI agent knowledge layer
This content was created by Insights, the custom content division of MIT Technology Review, and not by editorial staff. It was researched and written by humans, and any AI tools it may have used were limited to production processes under human supervision.
Source: www.technologyreview.com