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The design can make database answers more repeatable by fixing the query underneath them. But it leaves a consequential judgment with the model: selecting the right report and inputs.
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Oracle is proposing a governed alternative to live text-to-SQL: analysts predefine reports, while an agent selects among them and supplies typed parameters. The approach targets business-logic errors that produce plausible but incorrect answers, though report selection can still fail and many teams will retain text-to-SQL. An August 21 BIRD snapshot put the best model at 80.04% execution accuracy versus 92.96% for humans.
Fixed SQL makes analyst validation and report definitions the main control point, but does not guarantee the agent chose the right report.
Oracle cites BIRD execution accuracy of 80.04% for the best listed model versus 92.96% for humans; leaderboard positions may change.
Agents can inherit a user’s corporate permissions, with Deep Data Security enforcing row-, column- and cell-level policies.
AI agents promise plain-English answers from production data. Oracle’s design starts by limiting that promise: rather than let a model generate SQL live, it exposes pre-approved analyst reports as governed Model Context Protocol tools. The agent selects a report and supplies typed inputs such as a region or quarter.
Oracle is targeting a failure more dangerous than broken syntax: SQL that runs but applies the wrong business definition or table relationship. Its example is interpreting “month” as days since a customer’s first purchase rather than a calendar month. The result can look authoritative while measuring the wrong thing.
Oracle also cites an August 21 BIRD benchmark snapshot: the best listed model reached 80.04% execution accuracy, versus 92.96% for humans. The company notes that leaderboard positions can change; execution accuracy also does not settle whether a query used the right business logic.
The proposal makes analysts responsible for defining and validating the query in advance. With a fixed query, Oracle says the same question follows the same trusted path; a changed result should come from changed data, rather than different SQL generated by the model.
That does not make every answer correct. It moves a key model decision to choosing the relevant report and parameters. Oracle says relatively few organizations work this way today, it is still learning where the approach breaks, and many teams will continue using natural-language-to-SQL.
Oracle says an agent can authenticate through an existing corporate identity system and inherit the permissions of the person it represents, rather than receive a set of its own. Its Deep Data Security product applies SQL-declared policies at the row, column and cell levels, including for agent-mediated access.
Those controls do not eliminate prompt injection, Oracle says. They limit how much data an agent can reach if something goes wrong. Oracle cites a May NSA publication warning that MCP adoption had outpaced safeguards, and says more than 40 vulnerabilities had been filed against MCP implementations this year.
Oracle AI Agent Memory adds working memory, long-term factual memory and procedural memory to Oracle AI Database. Oracle argues that when agent memory contains company information, it should sit under the same access rules, backups and auditing as other enterprise data.
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