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The 2027 budget that the Trump administration released on Friday is in many ways a repeat of last year’s proposal: It includes deep cuts to the National Institutes of Health, the elimination of a health research agency, and the creation of a new agency devoted to chronic diseases called the Administration for a Healthy America.
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WASHINGTON — The Food and Drug Administration used the president’s budget to propose policies aimed at encouraging domestic development and manufacturing of drugs.
FDA Commissioner Marty Makary has said the agency needs “giant, big ideas” to counter China’s dominance in early-stage clinical development of drugs. Among the FDA’s ideas are proposals to make it easier to run early-stage trials in the U.S. and to hand an advantage to U.S.-based generics manufacturers.
The Trump administration has been using a variety of policy levers to try and bring drug manufacturing to the U.S. For example, many of the brand drugmakers that struck deals to lower U.S. prices also promised to increase domestic manufacturing, under the threat of tariffs.
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Companies that sell Medicare Advantage plans will receive a 2.5% pay bump on average in 2027, up significantly from what was proposed and a win for an industry that has experienced higher medical costs and has opposed nearly all reforms to the lucrative taxpayer-financed program.
More importantly, the Trump administration scrapped a proposal that would have used more updated data in the payment process, ensuring that Medicare Advantage insurers retain billions of dollars.
The finalized rate is estimated to add $13 billion in revenue next year for insurers, according to the Centers for Medicare and Medicaid Services. During trading after the markets closed, the stock prices of UnitedHealth Group, Humana, and CVS Health each climbed by more than 8%. Those companies are the three largest Medicare Advantage insurers, and together cover almost 60% of all people enrolled in the program.
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For the second time in two years, a bill is moving through the Colorado legislature that would exempt orphan drugs, which are used to treat rare diseases, from pricing caps that might be pursued by the state’s Prescription Drug Affordability Board — a panel whose work is being closely watched elsewhere in the country.
The effort reflects concerns that patients may lose access to these drugs if pharmaceutical companies halt sales of such treatments in the state. But opponents argue exemptions would unnecessarily extend to numerous big-selling medicines for common conditions that — thanks to regulatory endorsements — also happen to have an orphan designation.
As a result, consumer advocates complain the maneuver would only increase the risk that countless patients could have trouble paying for a wide variety of medicines. They further argue that the legislation would preserve profits for drug companies at the expense of the state government — and its taxpayers — as it tries to cope with budgetary strains.
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By law, the 2027 Medicare Advantage payment regulation must come out today. It will set the tone for how the Trump administration wants to work with the health insurance industry: as the “new sheriff in town” or just another friendly regulator.
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AI systems are starting to move beyond simple responses. In many organisations, AI agents are now being tested to plan tasks, make decisions, and carry out actions with limited human input. It is no longer just about whether a model gives the right answer. It is about what happens when that model is allowed to act.
Autonomous systems need clear boundaries. They need rules that define what they can access, what they are allowed to do, and how their actions are tracked. Without those controls, even well-trained systems can create problems that are hard to detect or reverse.
One company working on this problem is Deloitte. The firm has been developing governance frameworks and advisory approaches to help organisations manage AI systems.
Most AI systems in use today still depend on human prompts. They generate text, analyse data, or make predictions, but a person usually decides what happens next. Agentic AI changes that pattern. These systems can break down a goal into steps, choose actions, and interact with other systems to complete tasks.
That added independence brings new challenges. When a system acts on its own, it may take paths that were not fully expected or use data in ways that were not intended.
Deloitte’s work focuses on helping organisations prepare for these risks. Rather than treating AI as a standalone tool, the firm looks at how it fits into business processes, including how decisions are made and how data flows through systems.
Governance should not be added after deployment. It needs to be built into the full lifecycle of an AI system.
This starts at the design stage. Organisations need to define what a system is allowed to do and where its limits are. This may include setting rules around data use and outlining how the system should respond in uncertain situations.
The next stage is deployment. At this point, governance focuses on access and control, including who can use the system and what it can connect to. Once the system is live, monitoring becomes the main concern. Autonomous systems can change over time as they interact with new data. Without regular checks, they may drift away from their original purpose.
As AI systems take on more responsibility, it becomes more difficult to trace how decisions are made. This creates a demand for stronger transparency. Deloitte’s work highlights the importance of keeping track of how systems operate. This includes logging actions and documenting decisions. These records help organisations in determining what happened if something goes wrong. If an autonomous system takes an action, there needs to be clarity about who is responsible.
Research from Deloitte shows that adoption of AI agents is moving faster than the controls needed to manage them. Around 23% of companies already use them, and that figure is expected to reach 74% within two years. Only 21% report having strong safeguards in place to oversee how they behave.
Once an autonomous system is active, the focus shifts to how it behaves in real-world conditions. Static rules are not always enough, and systems need to be observed as they operate.
Deloitte’s approach includes real-time monitoring, allowing organisations to track what an AI system is doing as it performs tasks. If the system behaves in an unexpected way, teams can step in quickly. This may involve pausing certain actions or adjusting permissions. Real-time oversight also helps with compliance. In regulated industries, companies need to show that systems follow rules and standards.
In practice, these controls are starting to appear in operational settings. Deloitte describes scenarios where AI systems monitor equipment performance across sites. Sensor data can signal early signs of failure, which can trigger maintenance workflows and update internal systems. Governance frameworks define what actions the system can take, when human approval is required, and how decisions are recorded. The process runs across multiple systems, but from a user’s point of view, it appears as a single action.
Governance is part of discussions at AI & Big Data Expo North America 2026, taking place on May 18–19 in Santa Clara, California. Deloitte is listed as a Diamond Sponsor for the event, placing it among the firms contributing to conversations around how autonomous systems are deployed and controlled in practice.
The challenge is not just building smarter systems, but ensuring they behave in ways organisations can understand, manage, and trust over time.
(Photo by Roman)
See also: Autonomous AI systems depend on data governance
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The post As AI agents take on more tasks, governance becomes a priority appeared first on AI News.
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