The setup involves configuring the Agents using the Bee AI framework and integrating the Ollama model, along with generating the required API keys for secure and seamless operation. Once the environment is ready, the next step is to identify and define the use case to be automated using these agents. The process begins with designing the algorithm, which outlines the logical flow and decision points for automation. After the algorithm is finalized, the workflow code is discussed and implemented—this includes defining agent roles, interactions, and data flow within the system. Finally, a demo is conducted to showcase how the agents operate collaboratively to execute and complete the defined use case efficiently from start to finish.

CXOs are under increasing pressure to adopt Gen AI swiftly to maintain a competitive edge while managing the associated risks. Around 64% face investor and stakeholder pressure to accelerate adoption, yet ethical concerns remain a major hurdle—79% acknowledge the importance of AI ethics, but less than 25% have actually implemented ethical principles in practice. As a result, nearly 72% of organizations risk missing out on Gen AI benefits due to these concerns. Interestingly, three out of four executives now see ethics as a competitive differentiator. Each key stakeholder brings a unique perspective: CFOs worry about profitability, CMOs about brand reputation, CHROs about talent impact, CPOs about regulatory compliance, CROs about enterprise risk, CDOs about data integrity, and CEOs about overall accountability. Real-world cases like iTutor Group’s AI system rejecting candidates based on age and Amazon’s AI recruiting tool showing gender bias highlight the importance of addressing data and ethical issues before fully embracing Gen AI.