AI agents are moving from demos into production, and they need more than a model. They need real-time data, fast answers, and strict limits on what they can read, write, and act on. At the Agentic Data Summit on December 9, the session Guardrails, Not Guesswork shows how to lock down agent access with the Agentic Data Plane. You’ll also see how streaming, SQL, and agents run on one platform, and get a look at where the Agentic Data Plane is headed next. It’s free, virtual, and built for engineers taking AI agents to production.
We can provide an LLM with exhaustive information through our prompts, but it can still fail to use it properly. When the required information sits somewhere in the middle of a long prompt, this type of failure becomes even more likely. We call it the “lost in the middle” effect or an LLM blind spot. For example, imagine that we give an AI-based coding assistant a long collection of project documents. One specific paragraph explains that audit logs must be retained for 37 days. However, the coding assistant writes a cleanup function that deletes them after 30 days.
A study named “Lost in the Middle”, released in 2023 and published in 2024, studied this behaviour and found that performance was often strongest near the beginning and end. However, performance in the middle was weaker. This produces a U-shaped accuracy curve. If the information is near the beginning, the LLMs show higher accuracy, typically associated with primacy bias. If the information is near the end, the LLM...
Source: ByteByteGo · Summarized by HeadlinesBriefing