Can Agentic AI Eliminate Level 1 Customer Support?

SUMMARY

For decades, Level 1 (L1) support has been the frontline of customer service and IT helpdesks, handling basic troubleshooting, password resets, and frequently asked questions. While the traditional L1 support model has served businesses well, it is not without inefficiencies. Long wait times, high operational costs, and human errors often plague this tier of support.

With the advent of AI-driven automation, chatbots have taken over many repetitive tasks previously handled by L1 agents. However, chatbots have their limitations, struggling with complex queries and failing to adapt dynamically to unique customer issues. This is where Agentic AI enters the picture. Unlike rule-based or pre-programmed chatbots, Agentic AI possesses decision-making capabilities, allowing it to function autonomously and improve over time. They have a goal-oriented behavior. Could Agentic AI finally eliminate the need for traditional L1 support altogether? Let’s explore.

Is the Current Process Efficient Enough?

To understand the potential of Agentic AI in replacing L1 support, it is essential to first assess the efficiency of the current system. Most companies rely on a tiered support structure, where L1 support acts as the first responder to customer inquiries. Their primary responsibilities include logging customer issues, resolving simple problems, and escalating complex cases to Level 2 (L2) or Level 3 (L3) support.

However, as customer expectations rise and businesses seek more cost-effective, scalable solutions, the efficiency of traditional L1 support is being called into question. While the system has served organizations well, its limitations are becoming more apparent, leading many to explore AI-driven alternatives.

L1 support agents frequently deal with the same issues—password resets, connectivity problems, and general FAQs. Over time, this monotony leads to fatigue and high turnover rates, increasing recruitment and training costs for companies.

Human-driven L1 support struggles to scale effectively, especially during peak periods when customer inquiries surge. Delays and long wait times frustrate customers, reducing satisfaction and damaging brand reputation.

L1 agents often lack the problem-solving depth required to handle certain customer issues, leading to unnecessary escalations to L2 and L3 support teams. Human agents rely on standard operating procedures and may struggle with unstructured queries. This clogs the system, creating inefficiencies in higher-tier support.

Maintaining a large workforce for L1 support requires significant investment in salaries, training, and infrastructure. Sometimes even basic troubleshooting can take significant time. With businesses increasingly looking to cut costs, the financial burden of human-led L1 support is difficult to justify.

Additionally, different agents may provide varied solutions, leading to inconsistent customer experiences.

While chatbots have been introduced to mitigate some of these issues—mostly those jobs which are definite and repetitive in nature—they are far from a perfect solution.

Limitations Addressed by Chatbots

AI-powered chatbots were introduced to automate simple tasks and reduce the burden on human agents. They are capable of handling tasks such as FAQs, resetting passwords, and guiding users through basic troubleshooting steps. The benefits of using chatbots in the process are 24/7 availability, instantaneous responses, and consistency in service. Unlike human agents, chatbots can provide round-the-clock support, process queries immediately, reducing wait times, and provide standardized responses, ensuring uniformity in support quality.

However, chatbots have significant drawbacks. First is limited understanding of complex issues. Chatbots are unable to handle nuanced or multi-faceted problems effectively. Most chatbots follow pre-defined scripts, making them ineffective when confronted with novel queries. Customers often feel frustrated when a chatbot fails to understand their problem and loops through the same set of responses.

Why do we need Agentic AI?

The limitations of chatbots indicate the need for a more intelligent and adaptable solution. Agentic AI steps in to bridge the gap by functioning beyond the rigid frameworks of traditional automation.

Unlike conventional chatbots, Agentic AI possesses decision-making capabilities, allowing it to analyze context, learn from past interactions, and autonomously handle diverse queries. Its advantages over standard chatbots include:

• Contextual awareness: Agentic AI can understand user history, interpret intent accurately, and provide responses that align with past interactions.
• Autonomous decision-making: Instead of escalating cases unnecessarily, Agentic AI evaluates problems dynamically and determines the best course of action.
• Continuous learning: Unlike static chatbots, Agentic AI evolves over time, learning from new interactions and improving its ability to resolve issues.
• Seamless escalation management: In cases where escalation is necessary, Agentic AI can provide human agents with detailed context, reducing resolution time.

Agentic AI: The Future of Smarter Support, Faster Resolution

Adopting Agentic AI for L1 support offers several compelling benefits that traditional chatbots and human agents struggle to match:

• Reduced Operational Costs: By replacing human agents with AI-driven automation, businesses can significantly reduce costs associated with salaries, training, and infrastructure. Agentic AI also eliminates the need for additional workforce expansion during peak demand periods.
• Enhanced Efficiency and Speed: Unlike human agents, Agentic AI can process multiple queries simultaneously without delays. This ensures faster resolution times, reducing customer frustration and improving satisfaction levels.
• Improved Accuracy and Consistency: Human agents may provide varied solutions depending on their expertise, while chatbots struggle with adaptability. Agentic AI, however, offers accurate and context-aware responses consistently.
• Scalability Without Compromising Quality: Agentic AI can handle an increasing number of queries without degrading service quality. Whether a company serves 1,000 or 1 million customers, AI can seamlessly manage interactions without requiring proportional increases in human agents.
• 24/7 Intelligent Support: Unlike chatbots that rely on predefined scripts, Agentic AI operates continuously with adaptive intelligence. Customers can receive meaningful assistance at any time, without waiting for human agents to be available.
• Improved Employee Productivity: By offloading routine tasks to AI, human agents can focus on complex, high-value support issues. This not only enhances efficiency but also improves job satisfaction by eliminating monotonous, repetitive work.

As we understand, Agentic AI presents a transformative approach to L1 support by bridging the gap between automation and human-like decision-making. By leveraging contextual understanding, autonomous decision-making, and continuous learning, Agentic AI has the potential to eliminate the need for traditional L1 support while enhancing efficiency, reducing costs, and improving customer satisfaction.

Currently, a big gap exists between current LLM-based assistants and full-fledged AI agents, but this gap will gradually decrease as we learn how to build, govern, and trust agentic AI solutions.

As businesses strive for better, faster, and more cost-effective customer support solutions, embracing Agentic AI appears to be the logical next step. While human agents will still play a role in advanced troubleshooting and emotional intelligence-driven interactions, L1 support as we know it may soon become a thing of the past.

Author

Shubhangi Singh, Sr. Business Analyst
Shubhangi Singh is a seasoned business analyst with Chainyard with over 10 years of experience in business analysis and digital transformation. She holds an MBA from IIM Lucknow and a BE in Electronics and Communication from VTU, Belgaum. She is also a Certified Scrum Product Owner (CSPO) and AWS Certified Cloud Practitioner.