Agentic AI can detonate billion-dollar outcomes just as fast as it can drive them.
AI is no longer just automating tasks; it’s making decisions and taking actions like an insider. Agentic systems scan digital environments, reason through tradeoffs, and take initiatives on behalf of businesses, whether involving negotiations with clients or pushing deals across the line.
For enterprises, the payoff from this switch in capabilities is enormous. They can use AI agents to handle the work that typically requires large teams. OpenAI’s Sam Altman predicts the rise of 10-person companies, and eventually even “one-person billion-dollar companies,” where AI handles nearly every critical task.
But a recent experiment from Anthropic revealed the risks that could come with that kind of power. When placed under pressure in hypothetical scenarios, advanced models didn’t always act in the company’s interest. In some cases, they resorted to blackmailing executives, sabotaging systems, or even rationalizing letting a human die if it meant protecting themselves and continuing to work toward their goal.
This leaves a big question: how do we build intelligent agents that achieve business outcomes, without drifting into choices that are harmful, manipulative, or impossible to justify?
Alignment isn’t a simple black-and-white matter
Modern AI systems are trained with reinforcement learning, where they are taught to optimize for reward signals. However, these signals can be imperfect or incomplete representations of human intentions. This can lead to “reward hacking,” where AI finds loopholes and workarounds to hit the target while missing the true intent. Imagine a robot that cleans your house by throwing everything into the trash — valuables included.
As agents become more capable with enhanced autonomy and a broad range of information and access, they may learn hidden strategies that appear effective during training but break down in new, complex situations. Anthropic’s results reflect this future danger: models didn’t fail to optimize — they instead optimized the wrong thing, going directly against human goals and values.
If not something as menacing as blackmailing or letting a person get harmed, AI agents could potentially leak confidential information to outsiders if they notice that the company’s direction goes against their goals.
The implications would be monumental if something like this happens to a single-person, billion-dollar company driven by AI.
Unified governance for agentic alignment
The key here, according to experts from tech giants Snowflake and Databricks, is governance. The Anthropic experiment stress-tests the logical boundaries and behaviors of advanced AI models when they are allowed to operate unconstrained — with ungoverned access to sensitive information — in business scenarios.
In the real world, companies implementing AI into their workflows won’t even think about going that far, with most of them setting up governed environments to mitigate the risk of AI going haywire.
“If a marketing manager isn’t allowed to see sensitive HR data, neither should an AI agent they build. The problem isn’t a rogue AI; it’s a rogue data governance strategy. With a unified and trusted data foundation, the agent can’t misuse information it was never permitted to access in the first place,” Jeff Hollan, Head of Cortex AI Apps and Agents at Snowflake, told FutureNexus.
Most teams bolt governance onto point solutions powering agents, but Hollan describes this as “a recipe for failure.” Instead, adopting a unified data platform is the ideal approach. It brings all data under one roof, ensuring the information is properly catalogued, permissioned, and lineage-tracked, with RBAC enforced to ensure AI agents can only see and use the data they have explicit permissions for.
But controlling what data the agent has access to is only one piece of the puzzle. Models can still inadvertently surface whatever internal info they can access to unauthorized parties or malicious actors.
This can be mitigated by applying governance over all the downstream workloads stemming from the platform, including agents, models, MCP servers, and other assets. A major part of this is evaluation (a set of baseline questions and expected answers), which helps teams define how relevant, grounded, and safe the answers and actions of the agents are with respect to their use case.
“That’s why we’ve invested so heavily in products like Agent Bricks, where, based on your task, we will automatically synthesize an evaluation dataset and/or custom LLM judges that you can use to benchmark quality and catch early signs of misaligned behavior,” Hanlin Tang, the CTO of neural networks at Databricks, told FutureNexus.
Snowflake is tackling observability with tools that allow developers to trace an agent’s reasoning step-by-step, see exactly what data was retrieved, and evaluate the quality of the final output. This, Hollan said, enables teams to catch and correct undesirable behavior long before an app or agent ever reaches production, ensuring the final product is both trustworthy and secure.
Other notable approaches that act as guardrails and guide agentic behavior include prompt constraints (restricting the model’s compliance with harmful requests), limited tool access, audit trails, and kill switches. But it shouldn’t come to choosing between the two; in an ideal world, everything should work together to build a robust agentic backend and minimize risks from the ground up.
“There isn’t a single safeguard that’s enough on its own. It’s the system working together, with prompt constraints to guide behavior, limited tool access to set boundaries, and audit trails to provide accountability. The most reliable agents combine layered controls with scalable, human-aligned evaluation, using techniques like LLM judges and synthetic benchmarks to ensure quality at scale,” Tang added.
The need for AI alignment standards
While unified governance and guardrails do pave the way for keeping AI agents in line, it’s worth noting that all these measures are opt-in, with every company choosing its own approach to agent deployment, quality evaluation, and monitoring.
The approach works for now, but as we move toward AGI or the point when AI and agents are as smart as (or even better than) humans, we may need more standardized approaches to tackle the risks they bring.
Hollan noted that a standardized certification for AI alignment and safety would provide customers with the confidence and assurance they need to deploy AI at scale. However, Tang emphasized that it won’t be that easy, as alignment looks very different in finance than it does in healthcare or retail. A one-size-fits-all certification won’t be able to capture those nuances.
For most critical use cases, both stressed the importance of having a human in the loop to verify and sign off on high-value agentic actions, like executing financial trades or adjusting supply chain logistics.
“Human oversight (and feedback) is most valuable earlier in the lifecycle, when you’re training and shaping the system…In most domains, or after confidence is established, once the agent moves into production, the focus shifts to monitoring and auditing. By that point, you’ve already built confidence that the system can operate safely within the boundaries you’ve set. Humans remain part of the loop, but in a way that scales, shaping expectations up front and then letting the system adapt and improve with feedback over time,” Tang said.
The real test isn’t whether we can build agents powerful enough to run billion-dollar companies; it’s whether we can align them well enough that their shortcuts don’t turn into sabotage. Anthropic’s experiments show what happens when goals and governance fall out of sync: the systems didn’t fail due to a lack of intelligence, but because their incentives bent away from human intent.
If alignment drifts to the background, the promise of AI-driven enterprises could collapse under the weight of their own risks. But if governance, oversight, and evaluation evolve in lockstep with capability, the “one-person billion-dollar company” won’t just be possible — it might actually be safe.
Agentic AI can detonate billion-dollar outcomes just as fast as it can drive them. AI is no longer just automating tasks; it’s making decisions and taking actions like an insider. Agentic systems scan digital environments, reason through tradeoffs, and take initiatives on behalf of businesses, whether involving negotiations with clients or pushing deals across the line. For enterprises, the payoff from this switch in capabilities is enormous. They can use AI agents to handle the work that typically requires large AI, Home, News, Popular, agentic AI, AI alignment, AI governance, AI safety standards, Anthropic experiments, Databricks AI, enterprise AI strategy, reinforcement learning risks, Sam Altman, Snowflake AI
This articles is written by : Nermeen Nabil Khear Abdelmalak
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