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2023 can be described as one of the most eventful years for AI in recent times. It was full of action with the advent of large language models, generative capabilities, and the ability to engage seamlessly with users and offer unique, engaging, and exciting interactions. These developments continue to democratize AI and hold immense promise to make AI work for the benefit of humanity.
The year culminated with the arrival of multi-modal Generative AI models like GPT-4 Turbo/Vision and Gemini, with advanced capabilities to process text, image, audio, and video simultaneously and provide unified insights similar to the way humans perceive, understand, and process such information. This development has further inspired confidence to embark on research and development of Agentic AI systems.
What are Agentic AI systems?
Agentic AI systems are advanced artificial intelligence systems capable of pursuing complex goals with limited direct supervision by human beings. They are either an individual AI agent or a collection of AI agents working together in an autonomous mode in a clearly carved-out environment with definite scope to achieve set goals and objectives. They differ from traditional AI systems in multiple ways and following are some of their unique capabilities:
Purposeful, goal-driven, initiative-taking qualities
Agentic AI systems are not tactical and do not just react to input or instructions. They are strategic and have their own goals, take initiative, and actively pursue them to achieve desired outcomes.
Learning and Adaptability
Within a constrained environment and defined parameters, these systems can adapt to changing circumstances and make decisions on their own. They do not require constant human intervention and can learn from experience and adapt to situations to improve their performance.
Complex reasoning, decisioning, and planning
Agentic AI uses various algorithms that employ advanced reasoning capabilities, consider potential options/outcomes, and choose the best course of action to achieve specific goals.
How would an Agentic AI system manifest in the real-world?
The following is an example of how an agentic AI system would work in a customer service business function for a manufacturer of personal computing devices.
Perception and Understanding
Process internal data
The system would analyze chat, email, and phone interactions with customers regarding PCs, laptops, tablets, monitors, servers, and other products. It could access customer purchase history, warranty information, technical specifications, and other structured and unstructured knowledge repositories.
Augment with external data
The system would also monitor social media channels and online forums to understand customer sentiment, identify common issues, and track trends in technology preferences.
Personalize
Based on customer profile and their interactions, it would identify the customer’s needs, preferences, and expectations.
Goal Setting and Planning
The system can work with business goals/KPIs, monitor them, and constantly endeavor to improve. The following are some of the KPIs that can be tracked for agentic AI systems as they collaborate with human agents to improve overall performance:
First Contact Resolution (FCR) rate
Measure the percentage of customer issues resolved during the first interaction with the agentic AI, indicating its ability to handle routine and complex inquiries effectively.
Average Handle Time (AHT)
Track the average time it takes to resolve customer issues, highlighting the efficiency gains achieved through AI automation.
Customer Effort Score (CES)
Measure the effort customers must exert to get their issues resolved and the effectiveness of the AI system’s suggestions to make a task easy for customers.
Agent Productivity
Evaluate the impact of the agentic AI on agent workload, measuring the increase in time available for handling complex cases or proactive customer outreach.
Customer Satisfaction (CSAT) score
Monitor customer feedback and surveys to understand how satisfied customers are with their interactions with the agentic AI.
Net Promoter Score (NPS)
Track customer loyalty and willingness to recommend a brand, potentially boosted by positive experiences with the agentic AI’s helpful and efficient service.
Since an agentic AI system is goal-oriented, it typically adopts multiple strategies in a unified way to achieve its stated objectives.
Such a system enables:
Automated Technical Support & Troubleshooting
Resolving technical issues quickly
This could involve diagnosing hardware or software problems, guiding customers through troubleshooting steps, recommending drivers or firmware updates, and assisting with warranty claims.
Providing accurate and up-to-date technical information
The system could draw from the organization’s extensive knowledge base and technical documentation to answer customer questions about specifications, features, troubleshooting procedures, and warranty policies.
Resolving issues autonomously
The system could handle tasks like resetting passwords, updating billing information, and tracking order status without requiring human intervention. It could also engage multiple agents when the issues need multi-system interactions to understand the problem and resolve it.
Reducing agent workload
By handling routine inquiries, scheduling service appointments, and providing self-service resources, the system could free up human agents to focus on solving complex technical problems and building customer relationships.
Decision Making & Action Selection
Guiding customers through complex processes
When purchasing new products, setting up software, or configuring systems, the system could offer step-by-step instructions, video tutorials, and interactive co-pilots.
Identifying and escalating complex cases
Based on customer queries and system analysis, the system could seamlessly transfer customers to human agents for issues requiring deeper technical expertise or personalized support.
Proactive Service & Engagement
Proactive support and recommendations
The system could analyze customers’ usage patterns and predict potential issues, proactively notifying customers and suggesting preventive measures. It could also recommend compatible accessories, upgrades, or new products based on individual needs.
Personalized and empathetic service
The system could understand the emotional state of customers based on their current or recent interactions, review their purchase history, tailor communication style to their preferences, and reach out to them through their preferred communication channels at the right time. It could also recognize frustration and offer words of encouragement or apologies when appropriate.
Learning and Adaptation
Refine responses and recommendations
The system could learn from customer feedback, past interactions, and successful resolutions to continuously improve its accuracy and relevance.
Adapt to changing technologies and products
The system could regularly update its knowledge base with new product specifications, software releases, and technical documentation.
Personalize interactions and communication over time
The system could analyze customer preferences and communication patterns over time to adapt its tone, language, and style for a more natural and engaging experience.
Human Empowerment
Human-AI collaboration and shared goals
The agentic AI would not replace human agents but rather work alongside and augment them, acting as a powerful tool to enhance their efficiency, improve customer satisfaction, and ultimately build lasting customer loyalty.
Human experts remain crucial
The organization’s engineers and support specialists would still play a pivotal role in overseeing the system’s performance, providing training and feedback, and handling complex technical issues or customer escalations – consistently ensuring positive outcomes.
By implementing such an agentic AI system, the company could revolutionize its customer service landscape, offering faster resolutions, personalized support, and a seamless experience across all touchpoints. This would not only enhance customer satisfaction but also allow the company to focus its human capital on higher-value interactions and strategic initiatives, thereby driving overall business growth and success.
Will Agentic AI be the stepping stone to Artificial General Intelligence (AGI)?
Agentic AI, with its purposeful, goal-driven, and adaptable nature, is seen by many experts as a stepping stone towards achieving AGI, the ultimate form of artificial intelligence. While Agentic AI represents a significant step towards more advanced and autonomous AI systems, AGI remains a future possibility with potentially transformative implications. With more research and development and experiences from real-world use cases, such systems can gradually metamorphose to develop human-equivalent AGI capabilities like faster domain adaptability, advanced reasoning, problem-solving, creativity, innovation, self-awareness, and reflection.
As agentic AI becomes more prevalent, it will inevitably impact the way we work, live, and interact with technology. While such systems offer a lot of promise, they raise ethical concerns around autonomy, inherent biases, and transparency. They have the potential to revolutionize various aspects of our lives, but it is crucial to approach them with a thoughtful and balanced perspective, ensuring their advancement benefits humanity. Careful development and oversight are crucial to ensure these systems act responsibly and ethically. Guardrails need to be set so that they work under set parameters and constraints, their decisions are auditable, and they process human feedback and make needed adjustments/alignments as required.
Agentic AI is an ambitious endeavor to create next-generation autonomous systems and its success is likely to pave the way for AGI soon!
Author
Jayachandran Ramachandran
Senior Vice President – Artificial Intelligence Labs
Jayachandran has over 25 years of industry experience and is an AI thought leader, consultant, design thinker, inventor, and speaker at industry forums with extensive innovation and delivery expertise across a wide variety of industry verticals such as Financial Services, Semi-conductor, Consumer electronics, Automobiles, Aero, Manufacturing, Retail, Telecom, Medical Devices, Pharma, Energy, Utilities, etc. He has led AI practice and labs in leading organizations such as Infosys, IBM, and HCL; and has been instrumental in creating innovative solutions to solve complex business problems using Machine Learning, Deep Learning, and AI technologies. He brings in unique thought leadership and expertise of combining AI, cognitive neuroscience, and behavioral economics with a vision of democratizing AI thereby augmenting human capabilities, improving the overall experience, building trustworthiness, embedding ethical principles, and benefitting all to create a viable and enduring Human-AI world. He is responsible for defining and executing the overall AI and technology vision of the company with a specific focus on R&D, IP creation, and market differentiation. He holds a bachelor’s degree in Mechanical Engineering from the College of Engineering Guindy, Chennai.
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