The Context Problem: What's Really Breaking AI Agents at Scale
Show notes
As AI models become more powerful and affordable, enterprises still face a major obstacle to realizing the full potential of AI agents: the context gap. Without access to accurate, relevant information, even the most advanced models struggle to deliver meaningful results.
In this episode of The New Stack podcast, Sri Desikan, an executive at Elastic, explains why providing AI agents with the right context is essential for moving beyond impressive demonstrations to reliable enterprise automation.
Simply connecting agents to enterprise data through MCP servers isn't enough. Feeding massive amounts of information into large language models is costly, inefficient, and slow.
Elastic's approach centers on building an AI index that organizes enterprise data, metadata, and relationships, enabling agents to retrieve relevant information faster while reducing token consumption.
Desikan also explores how precomputed context can help prevent outdated information from influencing AI decisions. The conversation examines the implications for data privacy, agent autonomy, and the future of enterprise AI adoption.
Learn more from The New Stack around the latest developments in bridging the enterprise AI context gap:
The Real Bottleneck in Enterprise AI Isn’t the Model, It’s Context
Why enterprise AI breaks without metrics discipline
Why enterprise AI keeps stalling — and how data streaming could unlock it
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