Agentic AI
AI agents are the big AI trend this year, with a growing number of companies getting them into production. Check out our article to learn everything you need to know about getting started with building them and quickly getting ROI.
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Building an AI agent might sound like something only tech giants can pull off, but it’s becoming essential for organizations that want to stay competitive. AI agents are no longer futuristic concepts—they’re here and doing everything faster: solving real-world problems, optimizing traditional workflows, and helping businesses make smarter decisions.
In this guide, we’ll break it all down—from understanding what an AI agent does to the step-by-step process of creating your own.
An AI agent is a software program that can:
Unlike traditional software, AI agents can adapt and learn from their experiences. This makes them highly effective at handling complex tasks. They are used in various fields, including chatbots for customer support, autonomous vehicles, and virtual assistants. They generally handle tasks ranging from responding to inquiries to managing intricate systems.
To learn more about the concept of AI agents and their applications, check out this guide on AI agents.
A custom AI agent is a tailored software solution designed to meet specific organizational needs, integrating with existing systems to perform specialized tasks and achieve defined goals.
Custom AI agents offer organizations more than just automation—they bring tangible, transformative benefits. Here’s why you should build a tailored AI agent:
Custom AI agents excel at automating repetitive tasks and workflows. This can free up your team to focus on high-value activities like strategy and innovation.
A study by Vena Solutions found that customer service agents using AI tools handled 13.8% more inquiries per hour.
AI agents can process vast amounts of data significantly faster than traditional methods. For instance, Microsoft's AI forecasting tool, Aurora, can generate weather predictions in a fraction of the time required by conventional models.
This rapid data processing enables businesses to make timely, informed decisions, providing a competitive advantage in dynamic markets.
Similarly, AI agents can analyze medical data to assist healthcare organizations in diagnosing conditions or predicting patient outcomes.
By streamlining operations and minimizing manual work, AI agents can reduce operational costs significantly. For instance, Klarna, a Swedish fintech company, used AI to cut its sales and marketing expenses by 11% in the first quarter of 2024, translating to annual savings of approximately $10 million.
Custom AI agents can create unique capabilities and personalized customer experiences. According to Tidio, 80% of customers who have interacted with AI-driven customer service reported positive experiences. Additionally, companies offering excellent customer experiences can achieve revenue growth rates of 4% to 8% higher than their market average.
Creating an AI agent system may seem complex, but it becomes manageable when broken down into these nine steps. Here’s an in-depth look at each phase:
Start by identifying the specific problems your AI agent will address. What challenges in your organization need solving? For example, do you want to improve customer response times or automate repetitive tasks? Defining these pain points ensures the AI agent has a clear purpose.
Once the task is identified, set measurable goals to evaluate success. Use metrics like:
Align these goals with your broader business strategy, such as scaling operations or improving customer retention.
Developing an AI agent requires careful resource planning. Begin by assessing your budget. Remember to factor in the costs of:
Don’t forget to allocate funds for ongoing maintenance and updates.
Next, identify the tools and technology needed. Decide whether you’ll use pre-built platforms or custom solutions tailored to your needs. And of course, prioritize data privacy and security to ensure compliance with regulations like CCPA or GDPR.
There are different costs involved in building an AI solution, depending on your goals and scope. The range varies wildly. Expect to spend between $20,000 to $500,000.
A team can make or break your AI agent project, so this step is probably the most critical. Decide whether to build the team in-house or partner with external experts. External specialists can speed up development, while in-house teams often have a better understanding of your organization’s needs.
Key roles to include:
Ensure that all team members receive the necessary training to collaborate effectively and work with AI technologies.
Once you’ve assembled the team, identify the types of data your agent will need to perform its tasks effectively, such as historical customer interactions, operational logs, or raw data collected in real time. Ensure the data is accurate and clean by setting quality standards to avoid errors in the agent’s performance.
Next, establish a governance framework to assign ownership, define access controls, and implement policies for managing and updating data. It’s also crucial to ensure compliance with regulations like GDPR or HIPAA to maintain privacy and security, especially for health and finance industries.
Proper data planning gives your AI agent the reliable inputs to deliver accurate and actionable results.
When starting your AI agent journey, you'll likely navigate between building in-house capabilities and leveraging external solutions. To successfully build an AI agent in-house, you need to connect many different components, such as models by providers like OpenAI and Anthropic, orchestration frameworks like CrewAI, monitoring tools like LangSmith, and many more. However, combining components from different vendors often leads to hidden complexities and challenges.
As your AI initiatives scale, a fragmented multi-vendor approach can increase technical overhead, introduce security vulnerabilities from distributed data handling, and create reliability issues at integration points.
Consider comprehensive platform solutions like Arcee Orchestra, a turnkey platform powered by industry-leading language models that provide everything you need to build agent networks and workflows. The entire solution can also be deployed in the customer’s environment to meet the most strict security and compliance requirements.
Once you’ve chosen the right technology, it’s time to start building the AI agent. Begin by designing the agent’s architecture, which includes defining its workflows and determining how it will interact with users or systems.
Focus on developing core functionalities first, such as:
Implement natural language processing (NLP) capabilities if your agent will handle text or voice interactions. Additionally, set up API integrations to connect the AI agent with your existing software stack.
This stage ensures that the AI agent addresses the specific tasks it was built for while remaining flexible for future enhancements.
Testing helps to ensure that the AI agent performs accurately and reliably in real-world scenarios. Run controlled simulations that mimic your agent's tasks, such as answering customer queries or analyzing data.
Validate the agent’s performance by comparing its outputs to established benchmarks, like response times or accuracy rates. Address any issues by iterating based on feedback from technical teams and end-users. Testing should also focus on edge cases to identify potential flaws or limitations the agent might face under unique circumstances.
This rigorous process ensures that your AI agent is ready for deployment, and minimizes the risk of errors once it’s live.
After thorough testing, it’s time to deploy your AI agent. Start with a phased rollout to minimize risks and allow gradual adoption across your organization. This approach ensures you can address any initial issues without disrupting workflows.
Set up monitoring systems, such as dashboards and alerts, to track the agent’s performance and detect any anomalies early. Train end-users on effectively interacting with the AI agent, incorporating it into their daily operations. Finally, collect feedback from users post-launch to identify areas for improvement and guide future updates.
A well-planned deployment ensures a smooth transition and sets the foundation for your AI agent’s success.
Once your AI agent is deployed and stable, you can start thinking about scaling. Plan for integration with additional systems or platforms, such as expanding from handling customer inquiries to automating backend workflows. This scalability ensures your agent remains relevant as your organization grows.
Regularly analyze the agent’s performance to identify areas for improvement. Budget for ongoing updates, retraining, and enhancements to align the agent with evolving goals and industry advancements.
By following these steps, you can position your organization to maximize its capabilities, efficiency, and long-term value.
JPMorgan Chase, a leading global financial institution, has demonstrated the potential of AI agents through its deployment of the LLM Suite. This generative AI assistant improves employee productivity across various departments.
Challenge:
Employees at JPMorgan were spending considerable time on routine tasks, preventing them from focusing on more strategic initiatives.
Solution:
To address this, JPMorgan developed the LLM Suite, using large language models to automate these repetitive tasks. The AI assistant can write emails, summarize lengthy documents, and solve problems using tools like Excel.
Results:
This case underscores the importance of integrating AI solutions that align with organizational goals and that foster a culture of technological advancement.
Companies of all sizes are facing the decision of whether to implement AI agents now, or perhaps wait until the technology is more developed. We argue that there are significant advantages to acting now rather than waiting.
Early adopters of AI agents are already pulling ahead in their industries. By implementing AI now, you gain a competitive advantage that’s hard to replicate later. According to a McKinsey & Company report, businesses with advanced digital and AI capabilities generate shareholder returns two to six times greater than those that are lagging behind on AI. The longer you wait, the more you risk losing ground to your competitors.
The tools, platforms, and expertise needed to build AI agents are no longer reserved for tech giants. Advances in machine learning and platforms like Arcee Orchestra make it easy to build custom AI workflows. With Orchestra, for example, you can automatically route tasks to specialized small language models and create AI agents for complex tasks.
AI agents go beyond automating tasks. They deliver measurable results that make them a smart investment. Businesses implementing AI are achieving returns faster than ever, from reducing operational costs to improving customer satisfaction.
A 2024 Boston Consulting Group report highlights that leading companies expect nearly 50% greater cost reductions from AI adoption by 2027 than their peers. While a big initial investment is involved, the future cost reduction can be huge.
Consumer expectations are evolving, and AI is becoming the standard. By building AI agents now, you’re not just solving today’s problems—you’re preparing your organization for the future. Whether responding to dynamic customer needs or adapting to market changes, AI agents provide the agility to thrive in an unpredictable landscape.
And the great thing is that with AI agents, you can always start with small and start testing. AI agents require a relatively low initial investment of time and resources, and they also allow for quick iteration to find what really works for your business.
Every day you wait to integrate AI agents, you leave opportunities—and potential revenue—on the table. Want to see what AI agents can do for your business? Book a call with the Arcee AI team today and start building your organization's future.
To build custom agents, start by defining your business objectives and identifying the problems the agent will solve. Plan your resources, assemble the right team, and choose a suitable AI platform. Develop the agent’s architecture, test thoroughly, and deploy while preparing for future scaling.
AI agents are created using machine learning models trained on specific training data. Developers design the agent's workflow, integrate features like natural language processing, and connect it to APIs for broader functionality. Continuous testing and refinement ensure accuracy and performance.
The cost of AI agent development depends on factors like complexity, required features, and data preparation. For enterprises, costs can range from $20,000 to $500,000 and more, factoring in development, infrastructure, and ongoing maintenance. However, ROI often offsets initial expenses through efficiency gains and cost savings.
Building an AI agent isn’t just about keeping up with the times—it’s about positioning your business for long-term success. AI agents can transform how your organization operates, from automating workflows to delivering personalized experiences.
By starting today, you can gain a competitive edge, future-proof your business, and quickly see significant cost savings.
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