Contact Center Automation: 3 Tiers of Agentic AI-driven Support in 2025

2025 is shaping up to be a transformative year for contact centers. Artificial intelligence has evolved beyond simple chatbots or rule-based automation into what experts now call Agentic AI – autonomous, context-aware agents capable of performing complex support tasks. For contact center leaders, this shift is not just technological; it’s strategic.

Traditional customer support workflows rely on the familiar L1, L2, and L3 levels, each with distinct roles, responsibilities, and challenges. Yet, many organizations still struggle with fragmented knowledge, inefficient triaging, and slow response times across these tiers. Agentic AI promises to bridge these gaps, optimizing performance while enhancing the customer and employee experience.

In this blog, we’ll explore:

1. The functions and challenges of L1, L2, and L3 support.
2. How Agentic AI can automate and augment each support level.
3. The three tiers of Agentic AI and a practical 8-step implementation roadmap for leaders to follow.

Understanding the 3 Levels of Support: L1, L2, L3

To appreciate the impact of Agentic AI, it’s important to first revisit the traditional contact center model. There are 3 tiers of support: L1, L2, and L3. Each tier plays a significant role in customer support with its own functions. When implementing Agentic AI to transform customer support, the first step is to be well aware of the functions and challenges associated with these levels. 

L1 – Frontline / Basic Queries

Function:
Handles FAQs, password resets, account access issues, and simple troubleshooting requests. Agents at this level typically follow standard scripts and predefined workflows to ensure quick resolutions.

Challenges:

• High Ticket Volume: A majority of support traffic falls under L1, leading to overwhelming workloads and long response queues.
  
• Agent Burnout: Repetitive tasks with minimal cognitive engagement result in fatigue and low job satisfaction.
  
• Inconsistent Responses: Variations in knowledge usage and human error can lead to inconsistent customer experiences across channels.
  
• Limited Scalability: Human-only L1 support struggles to scale during surges, such as product launches or outages.

L2 – Technical / Contextual Issues

Function:
Addresses issues that require domain-specific knowledge, multi-step troubleshooting, and contextual understanding. L2 agents must interpret logs, use internal knowledge bases effectively, and interact with users to gather deeper insights.

Challenges:

• Knowledge Silos: Critical technical know-how often resides in scattered systems or individual experts, limiting accessibility.
  
• Escalation Bottlenecks: Manual handoffs between L1 and L2 extend resolution times and frustrate customers.
  
• Delayed Resolutions: Without unified access to contextual intelligence, agents spend significant time searching for the right fix.
  
• Skill Gaps: New or less experienced agents struggle to navigate complex troubleshooting without intelligent assistance.

L3 – Expert / Engineering-Level Support

Function:
Handles advanced technical challenges that require deep product understanding, root-cause analysis, and cross-functional collaboration with engineering or R&D teams.

Challenges:

• High Cost of Expertise: Skilled engineers are expensive resources and cannot be engaged for routine escalations.
  
• Long Mean Time to Resolution (MTTR): Investigations often involve data mining, log analysis, and testing, which are time-intensive.
  
• Limited Availability: Expert bandwidth is constrained, creating backlogs for critical incidents.
  
• Knowledge Retention Issues: Valuable insights from resolved L3 cases are rarely documented or reused efficiently, leading to repeated efforts.

Why this matters for leaders:
Each level is associated with specific KPIs — call handling time, first-contact resolution, escalation rate, and customer satisfaction. However, traditional processes are often slow, expensive, and prone to errors. Leaders need a clear view of where AI can create the greatest impact, not just to reduce costs, but to enhance the quality and consistency of service.

How Agentic AI Transforms L1–L3 Support

Agentic AI is not simply a faster chatbot; it’s an intelligent ecosystem that augments human agents, automates repetitive tasks, and enhances decision-making. Here’s how it can transform each support level:

L1 – Frontline Support: Fully Autonomous Resolution at Scale

Agentic AI operates as a fully autonomous frontline support layer, capable of understanding and resolving routine queries instantly.

• Automates Repetitive Queries: Handles password resets, status checks, and general troubleshooting without human intervention.
  
• Understands Context: Leverages Natural Language Understanding (NLU) to interpret user intent accurately across text, voice, and chat.
  
• Ensures Consistent Responses: Draws from validated knowledge bases, ensuring uniform answers regardless of channel or volume.
  
• Scales Effortlessly: Manages spikes in ticket volume during peak periods or product rollouts with zero downtime.
  

Outcome:
Ticket deflection of 60–70%, reduced workload for human agents, faster ticket resolution, and improved customer satisfaction — all while maintaining a consistent brand voice and tone.

L2 – Technical Support: Autonomous Problem Solving with Domain Intelligence

At this level, Agentic AI continues to operate autonomously, now empowered with domain-specific reasoning and adaptive problem-solving capabilities.

• Resolves Contextual Issues Independently: Applies learned patterns and historical data to resolve technical problems without escalation.
  
• Integrates with Knowledge Ecosystems: Connects to CRMs, ticketing platforms, and product documentation to find the most relevant and recent fixes.
  
• Learns Continuously: Uses feedback loops from resolved cases to refine its knowledge and improve resolution accuracy over time.
  
• Minimizes Escalations: Detects recurring technical issues and applies automated workflows for preemptive resolution, reducing L1-to-L2 handoffs.
  

Outcome:
A self-sustaining support layer that delivers intelligent, context-aware resolutions — freeing up human expertise for higher-value problem-solving.

L3 – Expert Support: AI Co-Pilots for Complex Problem Solving

At the L3 tier, Agentic AI acts as an intelligent co-pilot, augmenting human expertise rather than replacing it.

• Assists in Root-Cause Analysis: Mines historical tickets, logs, and incident reports to identify probable causes and recommend diagnostic paths.
  
• Provides Data-Driven Insights: Surfaces patterns from past resolutions, helping engineers detect anomalies or recurring issues faster.
  
• Enhances Knowledge Reuse: Automatically captures new findings and integrates them into the knowledge ecosystem for future learning.
  
• Collaborates Seamlessly: Supports experts during live investigations by fetching relevant documentation, suggesting next steps, and simulating outcomes.
  

Outcome:
Reduced mean time to resolution (MTTR), better knowledge continuity, and empowered experts who can focus on creative problem-solving rather than repetitive research.

Leader insight:
Agentic AI allows leaders to measure tangible business outcomes. For instance, Tier 1 automation directly reduces ticket volume and operational cost, while Tier 2 and 3 solutions optimize human expertise and accelerate resolution times. Crucially, AI works in synergy with humans, enabling agents to focus on complex, high-value tasks rather than routine, repetitive work.

Setting Up Agentic AI: 8-Step Implementation Checklist

Leaders need a structured approach to implement Agentic AI successfully. The following 8-step checklist provides a practical roadmap:

1. Define Goals & Scope

• Identify which support levels (L1–L3) to automate first.
• Align with business objectives: cost reduction, faster response, improved customer satisfaction.
  

2. Audit Knowledge & Data Sources

• Evaluate your knowledge base, ticketing data, and historical interactions.
• Clean, structured, and up-to-date knowledge is essential for AI accuracy.
  

3. Select AI Platform & Stack

• Choose an Agentic AI platform capable of orchestration, integration, and governance.
• Decide what to build in-house vs. leverage third-party AI solutions.
  

4. Design Workflows & Guardrails

• Define the rules for autonomous action and escalation paths.
• Ensure compliance, privacy, and operational safety.
  

5. Integrate with Core Systems

• Connect AI to CRM, ticketing, chat, telephony, ERP, and analytics platforms.
  
• Ensure seamless data flow for context-aware responses.
  

6. Train, Fine-Tune & Test

• Use historical tickets to train AI models.
  
• Conduct phased testing with controlled groups to validate performance.
  

7. Monitor KPIs & Optimize

• Track metrics like containment rate, AHT, MTTR, CSAT, and cost per contact.
• Continuously refine AI responses and workflows based on data.
  

8. Scale Across Tiers

• Begin with Tier 1 (L1 automation), expand to Tier 2 (L2 augmentation), and progress to Tier 3 (L3 autonomous agents).
• Iterate with feedback loops and continuous improvement.
  

Leader insight:
A structured approach ensures measurable results, mitigates operational risks, and allows leaders to plan adoption across tiers. Each step builds on the previous, ensuring AI integration complements human expertise rather than replacing it.

Key Takeaways for Leaders

1. L1–L3 support levels remain the backbone of contact centers, but AI redefines how each level operates.
   
2. Agentic AI is a strategic enabler, improving efficiency, consistency, and employee satisfaction while reducing costs.
   
3. Tiers of Agentic AI offer a roadmap for adoption: start with smart self-service (Tier 1), augment human agents (Tier 2), and achieve autonomous enterprise operations (Tier 3).
   
4. A structured 8-step implementation roadmap ensures a smooth rollout, measurable ROI, and sustainable adoption.
   
5. Human-AI synergy is critical: technology frees agents to focus on complex tasks while AI handles repetitive and time-consuming workflows.
   

“The future of contact centers is not just AI, it’s intelligent, autonomous, tiered AI that enhances every level of support. Leaders who adopt early will transform CX while optimizing cost and agent productivity.”

Conclusion

As the pace of digital transformation accelerates, contact center leaders face mounting pressure to deliver faster, smarter, and more efficient support. Agentic AI is ready to optimize operations across all support tiers.

By leveraging Agentic AI to automate and augment support levels, leaders can unlock measurable business value.  Those who embrace Agentic AI now will not only streamline their contact center operations but also position themselves as leaders in customer experience innovation for 2025 and beyond.

Are you ready to make the shift?

SearchUnify is ready to set you on the trajectory to success. With its Agentic AI suite, comprising AI agents curated to integrate into your support workflows easily, it is all your business needs to step up its support game. 

Request a demo today and see it for yourself.