Agents Aren't Stupid, They're Just Blind
Summary
AI agents are not inherently flawed, but they are 'blind' due to poor data context. The solution is not building smarter models, but implementing robust context engineering at the data layer. This involves moving away from designing data for single business problems (1:1 mapping) and instead building a unified, federated data context that preserves optionality. Key architectural patterns include stitching real-time data (like Apache Kafka) and long-term storage (like Apache Iceberg) into a single logical view, ensuring temporal consistency and comprehensive governance.
Key takeaways
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The Problem of Blind Agents
6:00
Agents can provide confidently wrong answers by relying on outdated, inaccurate, or incorrectly scoped data, leading to significant data loss even if human interaction costs are saved. (00:06:00)
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Data Access Limitations
10:22
Current methods—RAG (best for static corpora), MCP (good for fresh, wide-scope actions), and Skills (not designed for direct data access)—each have limitations that prevent a complete, accurate view of reality. (00:10:02)
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The Solution: Context Engineering
20:15
Instead of building for a single business problem, data architecture must be designed for 'context.' This requires federation and logical views to preserve optionality and allow agents to work with a broader, consistent data scope. (00:20:15)
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Achieving Temporal Consistency
26:18
A critical implementation pattern is stitching real-time systems (e.g., Apache Kafka) with long-term storage (e.g., Apache Iceberg) into a single logical view, ensuring data consistency across time spans. (00:26:18)
Technical details
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Data Blind Spots
360s
Four common failure modes include: 1) Using outdated/inaccurate data; 2) Wrong/over-scoping data; 3) Missing relationships between departments/products; and 4) Confidently answering when data is unknown. (00:06:00)
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Data Architectures
768s
The traditional Medallion Architecture (Bronze/Silver/Gold) is effective for 1:1 problem solutions but struggles with the complexity of agentic AI. (00:12:48)
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Data Access Patterns
622s
RAG is suitable for large, static corpora; MCP is useful for accessing fresh, wide-scope data via APIs; Skills are not designed for direct data access. (00:10:02)
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Federation and Logical Views
1215s
Federation allows issuing a single query that maps to multiple data sets and systems, delaying materialization and pushing processing as far left in the architecture as possible. (00:20:15)
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Data Governance
1975s
A unified governance context and overarching catalog (like OpenMetadata) are necessary to apply policies across federated data sources. (00:32:55)
Mentioned resources
- Streambased
- OpenMetadata
- Apache Kafka
- Apache Iceberg
Channel & topics
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