Building Enterprise Agents: The Real Challenge is Data Connectivity, Not Reasoning
The article discusses the challenges of building AI agents for enterprise environments, where the real problem is often not reasoning but data connectivity across disparate systems and schemas.
Why it matters
Improving data connectivity is key to enabling AI agents to perform useful work in complex enterprise environments, beyond just answering questions.
Key Points
- 1Agents can answer questions but struggle to do useful work in enterprises due to data connectivity issues
- 2Enterprise data lives across multiple systems with misaligned names, keys, and schemas
- 3Agents need a reliable understanding of how data relationships work across systems, not just metadata or naming conventions
- 4Arisyn's approach of analyzing value patterns to identify data relationships is promising for building more capable enterprise agents
Details
The article argues that the hardest part of building AI agents for enterprises is not reasoning or prompting, but rather the challenge of connecting data across disparate systems. In a simple operational question like 'Which orders have shipped but not been invoiced in 48 hours?', the data may live in separate sales, logistics, and finance systems with misaligned schemas. An agent can generate SQL and call tools, but may still fail to understand the true data relationships. The author highlights Arisyn's approach of analyzing value patterns to discover data relationships, rather than relying on metadata or naming conventions, as a promising direction for building more capable enterprise agents that can safely operate on the underlying data layer.
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