The missing data link in enterprise AI: Why agents need streaming context, not just better prompts
Enterprise AI agents today face a fundamental timing problem: They can't easily act on critical business events because they aren't always aware of them in real-time.The challenge is infrastructure. Most enterprise data lives in databases fed by extract-transform-load (ETL) jobs that run hourly or daily — ultimately too slow for agents that must respond in real time.One potential way to tackle that challenge is to have agents directly interface with streaming data systems. Among the primary approaches in use today are the open source Apache Kafka and Apache Flink technologies. There are multiple commercial implementations based on those technologies, too, Confluent, which is led by the original creators behind Kafka, being one of them.Today, Confluent is introducing a real-time [...]
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Across 101 enterprises, the context feeding AI agents is failing often and repeatedly. Sixty-eight percent have traced a confident but wrong agent answer to missing or inconsistent business context in [...]
Enterprise AI agents are only as reliable as the messiest documents behind them
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Claude’s next enterprise battle is not models: it’s the agent control plane
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Nimble claims its new, domain-specialized Web Search Agents cut token costs in half while boosting retrieval accuracy
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