How to Achieve Responsible AI Agents

We’re officially past the hype stage: AI is delivering measurable gains in the workplace. However, many of the AI tools we have seen take hold on a broad scale are designed to support tasks, not complete them autonomously. To achieve the productivity revolution that is the potential of artificial intelligence, we must pursue the power of agentic AI through a rigorously responsible framework. 

The first step in creating responsible agentic AI is understanding where these tools are best used. Our initial strategy for responsible deployment must target high-value, low-risk automation — what we term the “low-hanging fruit” of agentic AI. Some examples of use cases with the potential for incredible ROI include lead management, customer service, and sales assistance, as these tasks involve high-volume, highly structured workflows that naturally lend themselves to automation. 

Understanding the challenges of deploying agentic AI 

However, there are several use cases for agentic AI that are much more challenging. Tasks like compliance, insurance communication, and auditing represent the “High-Stakes” tier. While possible for AI agents, the high complexity, low tolerance for error, and necessity for a robust human-in-the-loop audit trail for legal and ethical reasons present significant barriers to trustless automation. 

Addressing the foundational barriers to trust and scale in agentic AI requires tackling: 

• Bias: One of the biggest challenges of any artificial intelligence-based system is bias. Because AI models are reliant on the data on which they are trained, they reflect any bias present in their training data. To ensure outputs are fair and unbiased, businesses must ensure that their models undergo extensive and supervised training with a diverse, representative dataset. Algorithmic bias is amplified in agentic systems, as their autonomous actions can operationalize and scale unfair outcomes. 

• Hallucinations: The tendency of generative models to produce confabulated or non-factual outputs remains a critical risk. In business use cases, this can be particularly detrimental. For example, if an AI agent assisting a sales team falsely offers a promotion or discount to a prospect, it could result in immediate financial liability and irreparable damage to client trust. 

• Data privacy: Depending on your business’s industry and use cases for agentic AI, there may be some particularly profound concerns about data privacy. If an AI agent is independently collecting and processing customer data, for example, it is crucial to take proactive steps by implementing zero-trust data architectures and comprehensive access controls to ensure regulatory compliance. 

For AI agents to be effectively deployed, it is important to put proper guardrails in place. If you think of an AI agent as a counterpart to a human employee, you wouldn’t allow a human employee to work without proper guardrails. You give human employees instructions and conduct audits to ensure their output aligns with expectations and the instructions that were laid out; why wouldn’t you do the same with AI agents through prompting and training? With agents, it’s not just about human oversight; it’s about instituting computational guardrails, such as constraint-based prompting, and leveraging Retrieval-Augmented Generation (RAG) to anchor the agent’s actions in verified, business-specific data. We also need to stop treating agentic actions the way we have historically treated deterministic system processes, especially when it comes to data access and manipulation. We need to handle these operations the way we would for a human employee – with access oversight and audit trails. 

While jurisdictions like the European Union have enacted landmark legislation such as the EU AI Act, the international fragmentation of these laws means that cross-border compliance cannot be outsourced to regulation. Consequently, companies must engineer compliance by design, focusing not only on meeting minimum legal thresholds but on building public trust through verifiable safety and transparency. 

The role of phased launches in the responsible deployment of  agentic AI 

Perhaps the best way to ensure the reliable deployment of agentic AI is to employ a phased launch approach: 

• Phase 1: Begin with a shadow launch, where the agent performs tasks in parallel with a human employee, but the AI’s output is not used.  

• Phase 2: When a human reviewer determines that 70-80% of the agent’s actions are both correct and fully compliant with predefined business rules, proceed to “human in the loop,” where the AI’s output is used with consistent review and feedback from a human operator.  

• Phase 3: After a high success rate in the “human in the loop” stage with minimal harmful consequences, it is finally possible to transition to a fully automated approach with only sporadic checks for accuracy and quality.

Taking a phased launch approach allows businesses to effectively train their agentic AI solutions to operate within the constraints and requirements of their systems and quotas. Although it is normal for AI agents to still have some challenges after deployment, a phased launch ensures they can be trusted before they are sent out on their own to make decisions that could affect the business. 

Ultimately, the name of the game in agentic AI is oversight and transparency. Any sensitive request or action that could have legal ramifications should be done with a “human in the loop” approach. As with any emerging technology, it will take time to address the issues that have arisen with AI technology, but ensuring that a human stays involved in these tasks can mitigate some of the concerns.  

While there are clear ethical and logistical concerns with the development of agentic AI, these issues can be mitigated or relieved entirely by taking a responsible approach to the technology’s development. Ultimately, the goal is not merely autonomous AI, but verifiably trustworthy AI. By anchoring our deployment strategy in a phased, human-centric approach, we are not just building tools; we are building the future of enterprise intelligence with the engineering rigor it demands.