Agentic AI is one of the hottest trending topics in the world of Generative AI today. But what exactly does it entail? Let’s break it down.
Agentic AI refers to systems made up of AI agents that possess the ability to make decisions autonomously, using inputs from multiple sources, and proactively adjust their strategies based on changing information. The level of autonomy can vary—ranging from semi-autonomous systems with human supervision, to fully autonomous ones. This blog from nvidia elaborates how the agents perceive, reason, act, and learn with feedback loops.
For example, a semi-autonomous agentic system tasked with booking flight tickets might search through multiple travel sites, check the user’s calendar, and weigh the trade-off between price and stopovers before presenting options to the user for final approval. In contrast, a fully autonomous agentic system might be used in a driverless car, where the AI must navigate the best path for a passenger based on factors like their preferences, traffic conditions, and terrain complexity.
Such systems bring several advantages due to their adaptive nature, which can lead to increased productivity and efficiency for users. Additionally, their decentralized architecture makes it easier to scale agents independently based on workloads. But, with these benefits come several challenges that need to be addressed.
In these agentic systems, multiple AI agents often work together, each responsible for different aspects of a task, but all ultimately collaborating to achieve a common goal. At the same time, there may be human oversight to monitor and guide these agents when necessary. This brings up the need for a structured approach to orchestrating agent behaviors. The inter-agent coordination is crucial, and any failures—whether agents fall short of their objectives, perform poorly, or get manipulated by rogue human users—must be managed effectively.
Several factors need to be in place to minimize harm at multiple levels. The agents themselves, the models driving these agents, and the users interacting with the system all need safeguards to ensure that adverse events do not occur. Implementing these protections, however, often increases costs and introduces trade-offs, particularly in areas like privacy and data security.
For instance, complex goals often involve multiple tasks and subtasks, and not all of these tasks may be invoked in every interaction. Testing every possible outcome of these behaviors could be prohibitively expensive. In such cases, prioritization becomes critical, and AI testers may need to adopt a risk-based approach—testing high-risk tasks first, such as those involving real-world safety like in autonomous driving. Real-world challenges are often difficult to anticipate; for example, how can we simulate all potential changes in terrain conditions for a self-driving car? And in cases like driving, human approval for every action is not always feasible or realistic.
Given the non-deterministic nature of many agentic systems working in tandem, it is essential to have robust guardrails in place. These guardrails should be implemented at various levels, starting with the data. Data used to train or fine-tune the models should be carefully cleansed to remove harmful content that could negatively influence the agent’s behavior. A mix of large, general-purpose models alongside smaller, task-specific models can help minimize the attack surface by limiting areas where the system is vulnerable.
Moreover, techniques like “Chain of Thought” reasoning can add transparency to the decision-making process of these AI models. This type of reasoning provides insight into how agents arrive at their decisions, making it easier to pinpoint potential flaws or areas where behaviors could go awry. It also aids in identifying moments when human oversight might be necessary, particularly in high-stakes scenarios, such as an autonomous car suddenly needing to apply the brakes or turning off cruise control on a complex highway stretch. This allows human experts to intervene and override the AI’s behavior when needed, ensuring that the system can gracefully degrade into a non-AI fallback mode in case of failure.
Testing should not be limited to data and models alone. Application-layer testing is also crucial to ensure that the agents interact safely and effectively with users, including appropriate guardrails on user inputs and prompts. For example, we need to make sure that the way users communicate with an AI agent doesn’t inadvertently trigger undesirable outcomes.
Adversarial testing can be a particularly effective approach here, simulating malicious inputs and edge cases to see how the agents respond. This form of adversarial simulation can help identify weaknesses before the agents are deployed into production.
Finally, once agents are in production, continuous observability is vital. Monitoring their behaviors in real-time and analyzing any emerging harmful patterns is crucial. As the environment in which these agents operate is dynamic, constant vigilance will help detect any anomalies or issues before they escalate.
Agentic AI holds tremendous potential, but it also comes with its own set of challenges. The balance between autonomy and human oversight, data security, and the continuous need for testing and monitoring are key considerations. By implementing strong safeguards, testing mechanisms, and ensuring ongoing monitoring, we can create robust, adaptive AI agents that are capable of tackling complex real-world tasks while minimizing risks.
Credits: This article is inspired by the research at OpenAI.