How Companies Build Agentic AI & Domain-Specific Models for Growth

TL;DR

• Agentic AI autonomously makes decisions in real-time, improving efficiency and customer satisfaction.
  
• Enterprise agentic AI enhances operations by automating tasks and offering domain-specific solutions.
  
• Adopting agentic AI boosts sales, retention, and overall business performance.
  
• Seamless integration of agentic AI optimizes existing systems with minimal disruption.
  
• Partnering with Convin allows businesses to leverage agentic AI for growth and competitive advantage.

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Call it the next AI evolution or a business survival strategy; agentic AI is here to reshape how modern enterprises operate.

In today’s digital economy, businesses are grappling with inefficiencies that legacy systems and generic automation simply can't fix. 

Contact centers struggle to maintain consistent customer experiences across channels. Sales teams waste hours navigating outdated CRMs. Compliance managers still rely on sample-based audits to catch violations—often after the damage is done.

A 2023 McKinsey report revealed that over 60% of companies deploying AI at scale still experience fragmented workflows, inconsistent decision-making, and low automation ROI. 

Why? 

Because they’re applying one-size-fits-all models to complex, domain-specific challenges.

Take American Express, which uses agentic AI to detect fraud and proactively personalize customer interactions. Their AI agents don't just react but initiate tasks, guide support reps, and adapt based on contextual data, reducing resolution time and enhancing security.

Stories like these underline a critical truth: without agentic AI and domain-specific models, businesses are flying blind in a data-driven world.

In this post, we discuss the potential of agentic AI, how it operates, and why businesses must embrace domain-specific models to drive innovation, enhance efficiency, and achieve tangible results.

Enable autonomous decision-making with Convin’s Agentic AI.

What is Agentic AI? 

Agentic AI represents a new paradigm in artificial intelligence—intelligent systems that don't just react but act with purpose. Unlike traditional AI, which responds to commands or data inputs, agentic AI systems can initiate actions, set goals, learn from context, and adapt in real time.

As IBM puts it, “Agentic AI refers to systems that act as autonomous agents—proactively making decisions, initiating tasks, and continuously learning from interactions to fulfill broader objectives.” 

In short, agentic AI is goal-driven, autonomous, and deeply contextual. It allows enterprises to scale intelligent decision-making without direct human oversight.

Core Capabilities

Agentic AI combines multiple technologies to deliver real-time value:

• Autonomy: Acts independently to initiate tasks and decisions.
• Goal-Oriented Behavior: sets and optimizes toward specific business outcomes.
• Memory and Learning: Remembers previous actions and outcomes to refine future behavior.
• Contextual Adaptation: Uses real-time data to adjust decisions dynamically.
• Multi-Modal Integration: Works across text, voice, and structured data inputs.

This makes enterprise agentic AI a game-changer for departments like customer support, sales, compliance, and collections.

Challenges Without Agentic AI and Domain-Specific Models

Without the power of agentic AI and domain-specific models, companies risk:

• Slow decision cycles due to manual processing.
  
• Low accuracy in generic AI models, especially in regulated industries.
  
• High operational costs from training, onboarding, and manual audits.
  
• Inconsistent customer experience across digital and human touchpoints.
  
• Compliance risks from missed violations or incomplete audits.
  
• Limited Scalability, as people-dependent processes bottleneck growth demands.

Ignoring agentic AI is like running a slow-motion race in today's fast-paced business world.

Enable autonomous decision-making with Convin’s Agentic AI. Try a demo today!

How to Build and Integrate Agentic AI into Your Enterprise Systems?

Building and integrating agentic AI into enterprise systems is a strategic decision to enhance operations and automate decision-making. 

By design, agentic AI works autonomously to make decisions, take actions, and learn from its environment, which makes it different from traditional AI systems. 

How Does Agentic AI Work?

Agentic AI operates by mimicking human-like decision-making processes but with the power to act autonomously and in real time. 

Unlike conventional AI, which simply responds to inputs, agentic AI initiates actions based on its learned goals and the data it processes.

Here’s how it typically works:

1. Data Collection: The system collects data from multiple sources, such as customer interactions, historical performance data, or external data feeds.
   
   
2. Contextual Understanding: Using natural language processing (NLP) and machine learning (ML) algorithms, agentic AI understands the context of a given situation, identifying patterns, intentions, and possible actions.
   
   
3. Decision-Making: Based on pre-defined goals and real-time context, agentic AI makes decisions that align with the business's broader objectives. This could range from recommending actions to taking direct steps to resolve an issue.
   
   
4. Autonomous Actions: Once the system has decided, it can initiate tasks, manage workflows, or guide human agents without requiring manual intervention.
   
   
5. Continuous Learning: Agentic AI constantly learns from its actions, feedback, and new data, which means that over time, the AI becomes more effective and precise in its decisions.

Step-by-Step Process for Developing Agentic AI Models

Building enterprise agentic AI requires a methodical, multi-phase approach to ensure the model is robust, efficient, and aligned with your business objectives. Below is a comprehensive guide to the process:

1. Define Business Objectives

Before you develop an agentic AI model, it’s crucial to identify the business challenges and goals AI will address. Whether it's automating customer support, improving agent performance, or enhancing compliance, a clear objective will guide the development process.

2. Data Collection and Preprocessing

Data is the lifeblood of AI, and it plays a crucial role in training agentic AI models. You’ll need to gather high-quality, relevant data. For contact centers, this may include:

• Customer interaction data (calls, chats, emails)
• Agent performance data (response times, resolutions)
• Contextual data (customer preferences, sentiment)

Once the data is collected, it must be cleaned and preprocessed to ensure it’s structured and ready for training. This step involves removing noise, normalizing values, and tagging the data to ensure machine learning algorithms can learn effectively.

3. Model Selection and Training

Selecting the right model is key to successful agentic AI development. Several algorithms, including reinforcement learning, supervised learning, and deep learning models, can be used to build enterprise agentic AI.

For agentic AI, reinforcement learning is often used. This method allows the AI to learn by interacting with its environment, receiving rewards for correct actions, and improving through trial and error. The model must be trained with vast data to understand patterns and make decisions based on real-world scenarios.

4. Testing and Simulation

Once trained, the agentic AI model must undergo rigorous testing in a controlled environment to ensure it behaves as expected. During this phase, you simulate real-world scenarios to check how the AI responds to customer inquiries, compliance requirements, or operational tasks.

Testing is crucial because agentic AI is designed to act autonomously, and any errors in decision-making can lead to significant consequences—whether that’s a poor customer experience or a missed sales opportunity.

5. Deployment and Integration

The next step is deploying the agentic AI model into your enterprise systems. Integration with existing software and infrastructure is crucial to ensure the system works smoothly across different platforms, such as CRM, cloud telephony systems, and customer service portals.

One of the main benefits of enterprise agentic AI is that it should integrate seamlessly with the tools agents and employees already use, minimizing friction and accelerating time to value. 

Convin, for instance, integrates with various CRM and telephony systems, enabling companies to leverage agentic AI without completely overhauling their existing setups.

6. Continuous Monitoring and Optimization

Once agentic AI is live, monitoring its performance and continuously providing ongoing optimizations is important. Continuous learning is at the heart of agentic AI. These models should evolve and improve over time as they interact with more data, resolve more issues, and adapt to changes in business processes.

Performance tracking tools, data feedback loops, and periodic retraining ensure that the agentic AI stays aligned with business goals and adapts to new challenges.

Agentic AI Development Services

While many companies can develop agentic AI in-house, working with an agentic AI development services expert can significantly speed up the process and provide tailored solutions. 

These services bring deep expertise in domain-specific models, advanced algorithms, and AI deployment. Partnering with an experienced AI provider means you can focus on business outcomes while leaving the complexities of agentic AI development to experts.

Convin offers AI-driven solutions specifically designed for contact centers. These help businesses automate agent coaching, quality monitoring, and performance analytics in real time. With Convin’s development services, enterprises can implement agentic AI seamlessly, improving productivity and customer satisfaction.

Consider American Express. They’ve successfully implemented agentic AI across their fraud detection systems and customer support operations. The AI identifies fraudulent transactions in real time and automates customer support responses based on a contextual understanding of customer needs. AI’s ability to make decisions faster than human agents has drastically reduced fraud cases while improving customer experience.

In conclusion, building and integrating agentic AI into enterprise systems is a step-by-step process that requires careful planning, advanced modeling techniques, and seamless integration. 

Maximize productivity and compliance with Convin’s tailored AI solutions.

How Can Domain-Specific AI Models Enhance Business Operations?

Domain-specific AI models are artificial intelligence systems designed and optimized to address the needs, language, and challenges of a particular industry or business domain. 

Unlike general-purpose AI, which is built to perform various tasks across various industries, domain-specific models are tailored to understand a given sector's unique context, terminology, and regulatory requirements.

For instance, a domain-specific AI model might be trained to understand customer service scripts, manage compliance with industry regulations, or even accurately predict customer intent in the contact center industry. 

This type of enterprise agentic AI is built to operate with a deep understanding of its environment, making it far more effective than a one-size-fits-all solution.

Why Are AI Models Specific to a Domain Important?

Domain-specific AI models address specific industries' unique language, challenges, and regulatory requirements, ensuring improved accuracy, efficiency, and compliance. 

For example, these models understand industry jargon, customer risk profiles, and regulatory language in financial services, enabling more precise decision-making. 

Trained on industry-specific data, they outperform generic models in pattern recognition, real-time scenario handling, and decision-making, resulting in faster responses and higher-quality interactions. 

Moreover, these models play a crucial role in maintaining regulatory compliance, such as ensuring HIPAA compliance in healthcare or following financial regulations in contact centers. 

By integrating these specialized capabilities, domain-specific AI helps businesses streamline operations, reduce risks, and enhance service quality.

Key Benefits of Implementing Domain-Specific AI in Your Business

1. Enhanced Decision-Making: Domain-specific AI models optimize business decisions by analyzing historical sales data, predicting customer behavior, and tailoring recommendations. This thereby enhances conversion rates and data-driven decision-making efficiency.
2. Personalized Customer Experience: Enterprise agentic AI, tailored for specific industries, enables companies to provide customized experiences, automate recommendations, and offer real-time suggestions to agents, improving engagement and customer satisfaction, as demonstrated by Convin's AI-powered contact center tools.
3. Operational Efficiency and Cost Savings: Domain-specific AI models automate specialized tasks, reducing human error and overhead and enhancing efficiency and cost savings in various industries, such as insurance, risk assessment, and fraud detection.
4. Scalability: Domain-specific AI enables businesses to scale operations without significantly increasing headcount, particularly in e-commerce sectors, by handling high customer volumes and automating responses.

By integrating agentic AI models trained on industry-specific data, enterprises gain a robust, scalable solution tailored to their needs. 

The agentic AI development services that focus on these tailored models offer businesses the ability to enhance their operations, reduce risks, and boost profitability and customer loyalty.

In conclusion, domain-specific AI models unlock the full potential of agentic AI by providing highly specialized, context-aware capabilities that drive accuracy, efficiency, and growth. Focusing on each industry's unique requirements, these models help enterprises solve complex problems faster and more effectively.

Automate decision-making and boost efficiency with Convin’s Agentic AI.

Why Convin is the Expert Partner for Agentic AI Development and Deployment

When it comes to agentic AI development and deployment, Convin stands out as the expert partner for enterprises looking to transform their contact centers and business operations. 

With a deep understanding of enterprise agentic AI, Convin leverages cutting-edge technology to offer efficient, context-aware AI solutions that deliver real-time, actionable insights. 

Purpose-Built Agentic AI for Contact Centers and Enterprises

What is agentic AI? Agentic AI is designed to act autonomously, making decisions and taking actions based on real-time data. This technology is critical for industries like contact centers, where decisions must be made quickly, accurately, and often with minimal human input. 

Convin’s enterprise-level AI is built explicitly for this purpose. The platform is optimized to streamline operations by enhancing agent performance, reducing response times, and improving customer experiences.

For example, Convin’s AI-driven tools in contact centers provide real-time transcription and agent performance insights, helping agents stay on track with customer queries and manage more complex situations effectively. 

By integrating agentic AI, companies can automate routine tasks like call logging, data entry, and compliance checks, allowing agents to focus on high-value tasks. As a result, businesses can reduce operational costs and improve overall efficiency.

Real-Time Coaching and Actionable Insights That Boost Agent Performance

One of the most powerful aspects of agentic AI is its ability to provide real-time coaching to agents during customer interactions. With Convin’s AI-driven platform, agents receive actionable insights during live calls, which helps them improve their performance on the spot. This boosts agent confidence and leads to better customer service outcomes.

For instance, Convin’s AI Assist feature provides live prompts to agents, helping them navigate customer queries with the correct responses, suggestions, or solutions. 

It also offers performance feedback, flagging improvement areas and actionable tips on handling specific situations. The system detects positive or negative customer sentiment in real time, advising agents to escalate an issue or continue handling it. 

This real-time coaching helps businesses improve customer satisfaction (CSAT) by ensuring that agents always deliver quality service.

Seamless Integration with Existing Business Ecosystems

Another significant advantage of partnering with Convin is its seamless integration capabilities. Agentic AI development for enterprises often involves integrating AI into existing CRMs, telephony platforms, customer databases, and more. 

Convin specializes in this integration, ensuring businesses can leverage their current infrastructure while adopting advanced agentic AI solutions.

Convin’s platform is designed to integrate effortlessly with various technologies, such as cloud telephony systems and popular CRM platforms like Salesforce, ensuring smooth workflows across the organization. 

This integration minimizes disruption, reduces the need for costly and time-consuming system overhauls, and accelerates the adoption of enterprise agentic AI.

Domain-Specific AI Models That Drive Measurable Outcomes

Domain-specific AI models are essential for industries that require specialized knowledge and deep contextual understanding. Convin takes this approach by developing AI solutions tailored to specific business sectors like e-commerce, finance, healthcare, and customer service. 

These models are trained on data specific to each industry, allowing them to understand the nuances, terminology, and regulations unique to each domain.

For example, Convin’s AI models in the healthcare sector are designed to understand HIPAA regulations, patient confidentiality, and medical jargon. In finance, the models focus on risk analysis and compliance with financial regulations. 

This domain-specific AI ensures that businesses can deploy AI-driven systems that are effective and compliant with industry-specific standards.

The measurable outcomes are clear: businesses using Convin’s agentic AI see increased sales, improved customer retention, reduced operational costs, and enhanced agent performance. 

For instance, Convin's clients typically report a 21% increase in sales, a 27% boost in customer satisfaction (CSAT), and a 25% improvement in retention rates. These tangible results demonstrate the power of combining enterprise agentic AI with industry-specific intelligence.

With purpose-built solutions for contact centers, real-time coaching tools, seamless system integration, and domain-specific AI models, Convin helps enterprises unlock the full potential of enterprise agentic AI. 

By implementing these advanced AI tools, businesses can achieve measurable improvements in efficiency, customer satisfaction, and bottom-line growth. 

Why Now Is the Time to Build with Agentic AI

Implementing agentic AI with domain-specific models can give businesses a clear advantage. By leveraging enterprise agentic AI, companies can automate complex decision-making processes, enhance operational efficiency, and deliver a more personalized customer experience. 

These AI-driven solutions are proven to drive tangible outcomes such as increased sales, reduced costs, and improved customer satisfaction—critical factors for success in today’s market.

Implementing these advanced systems becomes achievable and impactful with the right technology partner. Convin offers the knowledge, resources, and technology needed to ensure this change is smooth and long-lasting, opening the door to a quicker and more compelling future.

Streamline operations with Convin’s domain-specific AI models—request a demo!

FAQs

1. What are the key differences between agentic AI and traditional AI?
Agentic AI goes beyond simple data processing by taking autonomous actions and making decisions in real-time based on contextual data, unlike traditional AI, which typically responds to pre-defined inputs.

‍2. How does agentic AI improve customer service operations?
Agentic AI enhances customer service by providing real-time coaching to agents, automating routine tasks, and improving decision-making efficiency, which leads to faster response times and higher customer satisfaction.‍

3. Which industries benefit most from domain-specific AI models?
Domain-specific AI models are especially beneficial in the healthcare, finance, e-commerce, and insurance industries, where specialized knowledge and regulatory compliance are crucial for success.

‍4. How can Convin help businesses implement agentic AI?
Convin offers agentic AI development services that provide tailored solutions for contact centers. These services integrate real-time coaching, automation, and domain-specific models to enhance agent performance and operational efficiency.