Effective Tactics to Mitigate Risks in LLM-Driven AI Agents

How can businesses embrace the power of Agentic AI without compromising trust? This article delves into the challenges of deploying LLM-powered AI Agents and introduces effective design tactics to ensure accurate and professional interactions.

Considering the Right AI Implementation for Your Project

The advent of LLM-powered systems like Agentic AI offers unparalleled speed, efficiency, and personalization, transforming the customer experience. But with innovation comes risk—unmet expectations, misleading prompts, or inappropriate outputs can jeopardize customer relationships and enterprise reputation built over decades. 

The fundamental first step is to evaluate which use cases would benefit most from the incredible flexibility of Agentic AI and which should rather be implemented with more controlled and predictable NLU workflows. Oftentimes, your best bet is a composite approach combining the best of both worlds.

Whether you decide on a pureplay Agentic AI implementation or a composite approach, it is critical to be aware of the risks associated with LLM-driven systems and take relevant mitigation measures when designing AI Agents.

Recognizing the Risks 

LLM-driven systems, while transformative, are not without their challenges: 

• Misunderstanding Capabilities: Users might expect more than what AI can deliver, leading to frustration.
• Misleading Prompts: Some users intentionally test boundaries, probing the AI to respond inappropriately or adopt unconventional tones.
• Hallucinations: Despite advancements, AI can still produce misleading or incorrect outputs, which, in a regulated industry, could have serious implications for compliance and reputation.

Even though such risks might also occur with human agents, proactive measures are essential for mitigating them in AI Agents. 

Effective Risk Mitigation Measures

1. Model Selection 

The good news: modern language models are continually improving in managing interactions in a politically correct and responsible manner.  However, in industries like insurance, banking, or healthcare —where clarity and accuracy are non-negotiable—this progress sometimes comes at the cost of responses appearing overly cautious or even wishy-washy. To ensure your AI Agent aligns with your goals, it’s crucial to select the right model for your needs and – if necessary – fine-tune its behavior.  

While effective, fine-tuning requires substantial expertise and can incur additional costs, making it less accessible for some organizations. Thankfully, prompt engineering can often deliver similar results for many common use cases.  

Developing your own model, on the other hand, is even more ambitious than just fine-tuning. It demands a significant level of expertise and computational resources, especially for general-purpose models. While narrow-scope language models can be viable for specific enterprise applications, building a competitive, broad-use model remains out of reach for most organizations.   

2. Prompt Engineering  

Prompt engineering is a powerful technique for shaping your AI Agent’s behavior and output without requiring complex or costly customizations.  For enterprises, this method is especially valuable in deploying AI Agents that provide accurate, compliant, and engaging responses aligned with business objectives. 

In Cognigy, prompts can be sent directly to Large Language Models using the LLM Node. However, with AI Agents, this process becomes even more streamlined. You simply provide descriptions of your agents and their roles, then refine their behavior with additional instructions, such as:  

• Whenever you are asked something outside of your job, say you can't answer because it's not related to your job.  
• Never discuss competitors or prices.  

These types of instructions significantly enhance the likelihood of generating appropriate and consistent responses.  

Cognigy’s AI Agents take this a step further by offering a curated, thoroughly tested library of speaking styles and safety instructions. These pre-configured guidelines can be conveniently activated and adapted within the Agent Wizard.  Additionally, custom instructions can be seamlessly combined to create an AI Agent that perfectly fits the specific enterprise requirements, from regulatory compliance to customer engagement 

3. Built-in Filters 

All language models inherently minimize risks by offering cautious and softened responses, which help reduce the likelihood of offensive or inappropriate outputs. Additionally, some models include built-in filters that serve as an extra layer of security – working like an additional "Agent" to moderate interactions before delivering responses.

Different models handle filtering in significantly different ways. For example, Anthropic currently does not include a built-in filter and instead provides guidance on how to create a custom moderation agent. In contrast, Azure stands out by offering the most transparency and flexibility, including detailed filter feedback, configurable moderation settings, and even the option to disable moderation entirely.   

For organizations, ensuring accuracy and compliance in customer interactions often requires additional control over moderation. Fortunately, you can implement pre- and post-processing mechanisms across all models to filter content effectively, providing a customizable approach to meet the specific demands of your business.  

4. Customizable Explicit Filters  

If your chosen model lacks sufficient filtering capabilities, or if you want to add an extra layer of safety, you can either create your own agent to classify inputs and outputs or leverage the expertise of specialized companies. These customizable filtering mechanisms allow you to define and implement safeguards that go beyond the built-in options. 

 
For example, Microsoft additionally provides its content filter as a standalone service, which you can combine with language models from other providers to enhance moderation. This can be seamlessly integrated into your AI Agent using the preconfigured Microsoft Azure Extension and the Detect Jailbreak node. 

Alternatively, you can utilize services like Microsoft's Content Filter or tools for detecting Personally Identifiable Information (PII) directly via the HTTP node, just like any other HTTP-based service. For example, OpenAI offers a free moderation endpoint, while Lakera provides a commercial solution that generates detailed reports on input and output checks. However, the trade-off for this additional safety layer is the added latency it introduces. 

Conclusion  

AI Agents are revolutionizing customer service by transforming interactions into seamless and natural experiences. With Cognigy, organizations can benefit from vendor-agnostic model selection, prebuilt safety templates, and advanced moderation tools, all tailored to your business needs. 

Whether it’s filtering sensitive content or incorporating human oversight with Copilots, Cognigy ensures your AI Agents maintain the trust and reliability your customers expect. 

By combining cutting-edge technology with robust safeguards, you can confidently harness the power of AI to deliver exceptional customer experiences.