The Sectoral AI Governance Act of 2026 (SAGA) is a U.S. House bill, H.R.9125 in the 119th Congress, introduced by Rep. Sara Jacobs (D-CA-51) on June 3, 2026. Its official title is "To authorize the head of agencies to issue rules related to the uses of algorithmic decision-making systems that are likely to materially contribute to violations of Federal laws that the agency is authorized to enforce, and for other purposes." The bill takes a sector-by-sector approach: rather than creating a new cross-cutting AI regulator, it confirms and structures existing federal agencies' authority to write and enforce rules where AI-driven decisions contribute to violations of the laws those agencies already administer (Source: congress.gov).
Status and timeline
Jacobs introduced the bill on June 3, 2026. It was referred the same day to the House Committee on the Judiciary and, additionally, to the Committee on Oversight and Government Reform, each for consideration of provisions within its jurisdiction. Rep. Valerie Foushee (D-NC-4) joined as a cosponsor on June 4, 2026. As of late June 2026 the bill carried the status "Introduced," with no committee action beyond referral (Source: congress.gov). Its assigned policy area is Government Operations and Politics (Source: congress.gov).
Scope and provisions
The bill is built around federal agencies issuing new rules on algorithmic decision-making systems, with those rules required to avoid conflicting with existing federal law. As summarized in contemporaneous reporting, agencies acting under the bill would be required to: seek early public input through an Advance Notice of Proposed Rulemaking (ANPRM) before a rule is proposed; consider whether a new rule could unnecessarily disrupt government services or public benefits, and work to avoid such disruption; review existing rules and revise or repeal those that are outdated or no longer appropriate; and uphold a state's authority to regulate its own algorithmic decision-making systems unless that state regulation directly conflicts with a rule issued under the bill (Source: shrm.org).
In the press release accompanying the bill, Jacobs framed the measure around AI already shaping consequential decisions — "whether they get a loan, a job, or health care coverage" — often in what she called a legal "gray area," and described the bill as giving "federal agencies clearer authority to write and enforce rules when AI is used to break existing federal laws" (Source: shrm.org).
Position in the AI-regulation debate
The bill is one expression of a sectoral, enforcement-led model of AI governance — regulating AI through the agencies and statutes that already govern lending, employment, housing, and health care — as an alternative to a single comprehensive AI statute or a new dedicated regulator. That framing places it in tension with proposals for broad federal preemption of state AI laws, since the bill expressly preserves state regulatory authority except where it directly conflicts with a federal rule issued under it (Source: shrm.org).
The sectoral approach itself has drawn argument from the opposite direction. In June 2026, an analyst cited the Federal Trade Commission's rulemaking on "personalized pricing" — part of a broader fee-transparency effort aimed at online food-delivery services — as evidence that existing sector-specific enforcement can already reach AI harms, and argued on that basis that a measure like the Sectoral AI Governance Act is unnecessary (Source: insideaipolicy.com). The same enforcement-versus-legislation question runs through related debates over AI in employment decisions and algorithmic pricing.