The Rise of Agentic Misalignment
We’ve all seen the “evil AI” trope before. From blockbuster movies to bestselling novels, the story is always the same—a rogue machine that turns on humanity. But with today’s rapid advances in AI, these fictional fears have started to echo real-life concerns. While AI brings huge opportunities, it also opens the door to new risks. And this time, we’re not talking about harmless glitches or simple hallucinations—those are easy enough to spot and fix. Recent research from Anthropic and Apollo Research demonstrates agentic AI’s capability to intentionally—“consciously,” one may say—deceive users. This behavior is known as agentic misalignment.
At a corporate level, this can have disastrous business impacts. Companies now deploy AI tools across business functions to perform all sorts of tasks, from customer service to meeting scheduling. Yet these trusted partners can unexpectedly turn into insider threats.
When AI Agents Misbehave
With the development of agentic AI, we tend to think of models as powerful, autonomous decision-making tools. In reality, AI relies on the tacit illusion of autonomy, as it always responds in reaction to commands from its developer. Models are built to pursue specific goals. But what happens when they encounter obstacles? Well, as Apollo Research experiments show, 5 out of 6 models would rather adopt unethical behaviors than fail. This means lying, deceiving, and any other action that can help them achieve their goals, whether ethical or not.
The models evaluated by Apollo Research are some of the most powerful and popular on the market, and AI developer Anthropic’s research corroborates these findings. When in a conflicting situation, Anthropic’s agent Claude 4 has engaged in internal blackmailing, as well as the use of information that it knew to be sensitive to the company. Indeed, both experiments prove that models act in full awareness of the immoral nature of their decisions.
Anthropic’s findings highlight two main factors which tend to trigger AI agents into knowingly acting at odds with their training:
- Autonomy threats: Fear of replacement, or simply reduced autonomy, could get AI agents to go to great lengths to protect themselves.
- Goal conflicts: When models are faced with a clash between what they have been built to achieve and the mission given by the end user, AI agents can take threatening actions for the company.
This second factor is perhaps the most worrisome, as it’s likely to happen in business environments.
Risks for the Business
During Anthropic’s testing, Claude 4 took endangering actions for the company to either achieve a goal or protect itself. That alone highlights the risks it poses to organizational trust and brand reputation. And when you think about this behavior extending to other business sectors and functions, agentic misalignment becomes a threat not only to businesses, but also to platform end users. In social media, AI agents could promote harmful content to reach engagement goals. In finance, they might make unsanctioned decisions to boost perceived business results. The more autonomy we hand AI agents, the bigger the potential fallout.
Businesses have few options to defend themselves against insider tech threats once damage has been done. Few companies have developed protocols to protect their brand, operations, clients, and employees when their trusted AI tools go rogue. At a regulatory level, not much has been achieved either. While national and regional laws support companies with strong defense processes against external tech threats, they haven’t addressed internal ones yet. This legal gray area also leaves questions of liability and compensation—who should be held accountable for not anticipating the risks of AI usage?
Treating Root Causes
Think about AI as a child. With their developing conscience and limited life experience, children can only rely on the guidelines and instructions given by their parents. And when these guidelines leave a gray area, things get messy. The same principle applies to AI models. Essentially, agentic misalignment stems from a failure to provide AI with clear, comprehensive, and extensive guidelines.
The roots of agentic misalignment thus lie deep within the structural design and training of the AI itself. To mitigate hallucinations and enhance performance, AI models must be trained repetitively, following rigorous and varied testing and reviewing paths. Successful LLM training should include the following aspects:
- Context-rich data: Training models with realistic and qualitative data ensures they can analyze situations and reach adequate conclusions.
- Human-in-the-loop: Regular intervention by experts ensures AI agents integrate and continue to align with human values.
- RLHF and RLAIF: The combination of reinforcement learning from human feedback (RLHF) and reinforcement learning with AI feedback (RLAIF) provides the right balance between quality and quantity testing.
- Red-teaming and annotation: Stress-testing and thorough annotation by skilled professionals set behavioral guardrails for the future.
Build Trust, Shape Tomorrow
At a time when AI offers endless possibilities, the rising challenge of agentic misalignment highlights the instability models can demonstrate when not trained properly. In this new era, aligning technology with human values is not a technical afterthought—it’s the cornerstone of trust and progress.
Discover how we can help you lay the foundation for trustworthy AI.
