The rise of AI has highlighted areas where it can make the most impact with Customer Support operations consistently ranking as a front runner. Much of the discussion surrounding AI adoption in CX right now is focused on two key areas: Agentic AI and AI Agent Copilots.
Why all the buzz?
Agentic AI is the driving force behind the 10x productivity and efficiency organizations are experiencing because it immediately contextualizes customer information in real-time and acts on it…without any human input or direction required.
Agentic AI refers to autonomous AI agents, also known as digital assistants, that can adapt, make decisions, and take actions independently based on real-time contextual data and workflows, continually improving through feedback and optimization.
What’s equally as important to point out is that not all Copilots have Agentic AI at their core.
While some Copilots have cognitive capabilities, many are designed as decision trees that operate within defined constraints, that focus on automating a single business process.
Breaking Down The Types of AI Copilots
Most support teams have used some form of automation within agent workspaces, such as classic RPA, task automation, or unidirectional sync between systems. However, those looking for a Copilot need to think beyond case summarizations and agent responses.
While these are good starting points, true productivity gains from AI—with clear cost/benefit advantages—can be achieved through deep insights, resolution recommendations, and continuous learning for complex issues.
Across CX right now, nearly every company is offering a Copilot but, not all are the same. For example, here are 3 varying types of Copilots in the market.
Basic Copilots And Their Capabilities
- Basic AI Copilots in customer support begin with summarization capabilities. They quickly condense one source of customer data into concise summaries, helping agents grasp the essence of a conversation instantly.
- Summarization is useful when agents need to handle multiple cases at one time. But the onus is still on agents to reply to the customer and decide what steps to take to resolve their issues and challenges.
- Another function of basic Copilots is that of response generation. Basic Copilots assist agents by suggesting possible replies based on predefined templates, tones or other insights.
- However, these responses are often generic and focused more on maintaining positive, grammatically correct communication rather than resolving the underlying issue and required steps to help the customer.
The next level of Copilots that Support leaders can choose from are Extendable Copilots.
Extendable Copilots And Their Capabilities
- Moving beyond basic Copilot functionalities, Extendable Copilots provide information retrieval which helps increase agent productivity.
- Extendable Copilots pick up this information from multiple knowledge sources.
- Extendable Copilots are also able to highlight knowledge gaps and they can generate knowledge articles, product feature requests, etc
However, when it comes to case resolution, there is still a lot of reliance on human oversight as they with Extendable Copilots.
Advanced, Autonomous Copilots with Agentic AI such as TheLoops
- Advanced AI Agent Copilots shift the focus from just assisting with responses and handling simple requests to actively driving resolutions particularly in complex scenarios. They pair information retrieval with automation and a resolution graph to help agents in resolving complex cases.
- They can perform all of the functionalities of Basic and Extendable Copilots while also analyzing past cases and resolutions and learning how to improve with feedback from their human agent counterparts.
- At their core, advanced AI Agent Copilots can understand the symptom of a customer case, provide a resolution and simultaneously trigger an action and workflow all on their own.
- By leveraging historical data and AI, advanced, autonomous Copilots guide agents toward the most effective solutions, reducing resolution times and significantly improving customer satisfaction.
- Moreover, Advanced AI Agent Copilots go beyond just resolution summaries; when paired with AutoQA, they offer real-time coaching and quality improvement guidance.
As you explore all of the Copilot offerings in the market, the key question to ask yourself is, ‘Which ones actually leverage Agentic AI and which ones are still dependent on rule based and NLP?’
As a bonus, Agentic AI can scan and scour your help articles, past case resolutions, FAQs and so much more–no matter how vast or varied these assets may be.
This is why it’s vital to understand the differences between Basic Copilots, Extendable Copilots and advanced AI Agent Copilots such as TheLoops.
Exploring Agentic AI Further
Unlike passive AI tools that simply provide recommendations or summarizations, Agentic AI can handle complex cases and actions. It works based on predefined goals, rules, or learned behaviors and can independently initiate actions to achieve specific objectives, often in dynamic and complex environments.
The key characteristics of Agentic AI include:
- Autonomy: Agentic AI can function independently, making decisions and taking actions without constant human intervention. This autonomy can vary in degree, from simple automation to more complex decision-making capabilities.
- Goal-Oriented Behavior: Agentic AI is designed to achieve specific goals, whether that’s optimizing a process, responding to customer inquiries, or managing resources. It has memory and can adjust its strategies based on feedback, learning and changing conditions to meet these goals.
- Adaptability: Because it has memory, Agentic AI can learn from interactions and experiences, enabling it to adapt to new situations and improve its performance over time. This learning can be based on machine learning algorithms, reinforcement learning, or other AI techniques.
- Contextual Awareness: Agentic AI can understand and interpret the context in which it operates, allowing it to make more informed decisions. This might include understanding user preferences, the current state of a system, or external factors that impact its tasks.
- Interactivity: Agentic AI often interacts with humans or other systems, providing suggestions, taking actions based on input, or even collaborating with human users to achieve a shared objective.
Simply put, Agentic AI moves beyond basic automation by adding layers of decision-making and adaptability, enabling it to function as an assistant and collaborator across various domains including customer support.
Agentic AI and The Enterprise
The LLM landscape is evolving rapidly with the impending release of GPT-5/Llama3, which will raise the bar. Simultaneously, multiple models with GPT-4-level performance are now available at attractive price points. Enterprises can choose from models from different sources, with varying cost-performance levels, based on their use-case and functionality needs. That said, it’s crucial to pick a vendor or platform that offers the flexibility to adapt to your changing business needs.
Some of the key reasons why Enterprise companies are increasingly exploring Agentic AI Autonomous Copilots include:
Efficiency Gains: Agentic AI can automate repetitive and low-value tasks, freeing up agents and managers to focus on more strategic and critical activities.
Scalability: Agentic AI can standardize processes and ensure that knowledge workers have access to the same level of support and information, regardless of location, training or scale.
Employee Experience and Retention: Agentic AI can reduce the cognitive load on employees by handling a plethora of tasks without their involvement, leading to a more fulfilling work experience. This, in turn, helps reduce burnout and increases retention among agents.
Cost Optimization: By automating routine tasks and improving agent efficiency, enterprises can optimize costs, reducing the need for additional hires or, alternatively, ensure that new hires can ramp faster and perform at the same levels of their tenured peers.
Personalization and Customization: Agentic AI can tailor information, tools, and workflows to the specific needs and preferences of customers, enhancing their effectiveness and satisfaction.
Future-Proofing the Workforce: As Agentic AI becomes more integrated into business processes, companies that adopt it are better positioned to future-proof their workforce, ensuring that employees are equipped to work alongside advanced technologies.
Read how Gainsight was able to boost efficiency and save agents 300 hours per month with TheLoops AI Agent Copilot.
Take this complex billing inquiry for a B2B SaaS company as an example of how Agentic AI works.
Step 1: Initial Customer Interaction
The Customer initiates a live chat or call to inquire about an unexpected charge on their SaaS subscription bill.
- Support Agent: Receives the inquiry in their dashboard.
- AI Agent: Immediately identifies the type of query (billing-related) and pulls up the customer’s account, previous interactions, billing history, and subscription details, ready for the human agent.
Step 2: Contextual Summary
- AI Agent: Summarizes key details (e.g., the customer has been charged twice in one month due to a recent plan upgrade) and presents this information to the support agent in a quick overview.
- Support Agent: Glances at the summary, avoiding the need to manually sift through account records.
Step 3: Suggested Solutions
- AI Agent: Suggests a set of potential solutions based on similar resolved cases, such as issuing a refund, providing a credit, or explaining the new billing structure. It offers the pros and cons of each option.
- Support Agent: Reviews the suggestions, selects the most appropriate solution (e.g., issue a refund), and personalizes the response for the customer.
Step 4: Automating Routine Tasks
- AI Agent: Automatically initiates the refund process and generates an email confirmation to the customer, freeing up the support agent from administrative tasks. It also ensures the updated billing records reflect the change.
Step 5: Continuous Learning and Improvement
- AI Agent: After the issue is resolved, the AI updates its knowledge base to include the newly learned billing scenario, further improving its understanding for future inquiries.
Step 6: Escalation Handling
If the customer raises a new, more complex issue (e.g., confusion about specific charges that requires further investigation):
- AI Agent: Immediately offers to escalate the case to a higher tier or specialized billing team, while compiling all related documents, chat logs, and case history for the next agent in line.
Step 7: Post-Interaction Summary and Analytics
- AI Agent: After the call/chat is closed, it creates a detailed summary of the interaction for future reference and generates an automated post-call survey to gauge customer satisfaction.
- Support Agent: Can now move to the next ticket without spending additional time documenting or closing the previous case.
AI Agent Copilots: A Wide Spectrum, Choose Wisely
A clear understanding of Agentic AI and selecting the right Copilot built on it can be the key to maximizing your ROI when implementing AI for CX operations.
According to TheLoops CTO and Co-Founder Ravi Bulusu, who holds 26 patents for AI and machine learning, “Effective AI Copilots are more than just digital assistants – they’re sophisticated problem-solving partners. These AI detectives sift through oceans of data, unearthing critical insights that dramatically accelerate case resolution. While basic copilots might suggest responses or facilitate information retrieval, truly advanced systems go further. They guide our agents step-by-step through complex issue resolution processes, turning good support into great support. This is the future of customer service – where AI doesn’t just assist, but actively elevates the capabilities of our human agents.”
To resolve customer issues with precision and without compromising quality, your agents need a Copilot that is Agentic, one that can provide them with the immediate steps required to solve complex issues while also giving managers direct understanding as to where the agent may fall short through AutoQA.
Make sure to drive your choices based on desired business outcomes. The market offers various platform-level options. Incumbents are embedding AI or offering Copilots to accelerate time-to-value for users. Meanwhile, startups, scale-ups, and LLM providers are taking an AI-native approach to reinvent vertical use cases or create new platforms that transform cost, performance, and user experience.
Prioritize workflow and performance benchmarks. Ensure that security and governance are included as part of the platform without additional charges. The outputs of models should be transparent, not black boxes. Look for platforms that can learn and train from human input, allowing for continuous improvement within the AI frameworks.
Agentic AI Copilots Are Your Path Towards Productivity: Final Thoughts
Thanks to the wide stream adoption of ChatGPT, the majority of Support leaders are familiar with Large Language Models and AI prompts. Agentic AI goes way beyond that.
Agentic AI has memory and understands what’s been done in the past and learns how to suggest a more productive path forward. It also has built-in governance to ensure it adheres to any privacy, data or other protocol to adhere to.
While many of today’s Copilot options capitalize on assisting agents, very few offer all of the features, robust functionality and productivity gains of Agentic AI.
As we close this out, we’ll recap the points made throughout this blog:
- Selecting the right Agentic AI and AI Agent Copilot for your team is essential. You want to invest in a solution that will take you and your team forward and leapfrog your results vs crawl along.
- Treat this like training a new agent on the job—it’s not about accuracy on day one. AI agent performance directly correlates with the quality and relevance of its training data. You don’t need to wait for perfect data to get started with an AI solution. Even if it’s only 60% accurate initially, it will improve over time as it learns—just like a new agent gaining experience on the job.
- As a bonus, you’ll want a Copilot that can generate and update knowledge with each case closing and provide your agents the ability to ASK the Copilot questions.
TheLoops AI Agent Copilot does all that we’ve described and more. Ensure your team’s productivity and efficiency through our unique blend of AutoQA and our Agentic AI. Need a little more convincing? ; )
