Agentic AI Is Changing Everything: Who Can Actually Build It?

Agentic AI is expected to be everywhere soon. A Capgemini Research Institute report shows that 61% of organizations are exploring the deployment of artificial intelligence agents. Agentic AI, unlike the AI chatbots we are familiar with, is able to integrate with software systems to complete tasks either independently or with minimal supervision. 

This makes it an attractive tool for organizations to maximize productivity. These agents are expected to generate around $450 billion in economic value through revenue growth and cost savings by 2028. Around 38% of organizations are also likely to have AI agents as team members within human teams.

However, despite the rise in adoption of agentic AI, maturity has remained low. Only 2% of organizations have deployed this technology at scale. Around 12% have deployed it on a partial scale. One major reason is that most companies lack the expertise required. 

One solution for this comes in the form of AI consulting firms and AI automation agencies that help organizations build and deploy agentic artificial intelligence to automate their work.

What Makes Agentic AI Different?

To understand why agentic AI is gaining traction, it’s important to explore how it differs from traditional AI systems.

​There is no universally agreed-upon definition for agentic artificial intelligence; there are broad characteristics associated with it. While generative artificial intelligence generates text, images, etc., using large language models, agentic artificial intelligence goes further and makes decisions the way humans do, according to MIT Sloan associate professor John Horton. 

Artificial intelligence agents use standard building blocks, such as APIs, to communicate with other agents and humans to perform difficult tasks, including executing multi-step plans, using external tools, interacting with the digital environment, etc.

Businesses now use this technology for various multi-step tasks. For example, banking and financial service organizations are exploring the use of agentic artificial intelligence to detect fraud, automate loan approvals and compliance processes, and provide customized financial advice. 

Retail companies use it to automate the shopping experience, customer service, and other business activities, such as merchandise planning. It is also used to analyze vast amounts of data and documentation in markets with high-stakes transactions, such as real estate or investing.

Why Companies Struggle to Build It​

Despite the growing buzz around this technology, most companies still struggle to build it, which requires expertise in AI orchestration, integration, security, and automation. Around 86% of leaders in the IT industry believe that without proper integration, this technology adds just complexity and value, so it is important to ensure it is integrated and implemented correctly.

Some of the factors that create hurdles while building and implementing this technology are as follows:

• Artificial intelligence agents need high-quality, consistent data to function effectively; data landscapes are often fragmented. Datasets might also be incomplete, biased, or inaccurate, undermining agent performance.
• Connecting them with existing enterprise systems can prove challenging due to a lack of system compatibility, multi-protocol issues, and system dependencies.
• Reliability and performance can be hard to maintain due to the non-deterministic nature of outputs, cascading error propagation, and the production-readiness gap.
• This technology introduces new vectors that challenge existing security systems.
• The dynamic, probabilistic nature of artificial intelligence agents often challenges standard software testing approaches.

These challenges can only be met with specialized knowledge, and so most businesses are unable to handle them on their own. That’s where agentic AI consulting firms come in. By partnering with such companies, businesses can effectively overcome these challenges and implement the technology. 

The Growing Role of AI Partners ​

According to reports, 96% of IT leaders believe artificial intelligence agent success depends on seamless integration. However, most companies cannot achieve this alone, so they rely on partners that help build agentic artificial intelligence, provide AI integration services, and ensure the tool is deployed correctly. These firms help companies overcome challenges like ensuring there is sufficient data quality and accuracy, implementing system integration, and maintaining security and privacy.

These automation agencies bring in the expertise that most companies may otherwise lack. Platforms like Techreviewer can be used as a resource to explore companies that offer agentic AI consulting and implementation services, helping businesses understand the available expertise.

What Businesses Should Look for

Given the importance of implementing agentic AI thoughtfully and seamlessly, businesses should carefully evaluate potential partners. ​There are several qualities firms should look for when selecting AI agent development companies to work with. Some of those include:

• LLM Expertise: Large Language Models (LLMs) are different from autonomous agents; LLMs merely generate text, while agentic artificial intelligence actively makes decisions and takes actions. However, large language models are an important part of agentic artificial intelligence. They provide the various capabilities agents require to function autonomously. So, expertise on this is essential for companies building and implementing agents.
• Automation experience: Companies offering agentic AI development services need experience in automation, since that is what transforms static, conversational chatbots into autonomous, goal-oriented systems capable of performing complex, multi-step jobs.
• Integration capabilities: Building agents is only part of the challenge. They also need to be properly integrated to function. A good implementation partner will be able to help integrate agents with enterprise systems, tools, and data sources so they can perform tasks.
• Scalability: In order to make full use of AI agents, they need to be implemented at scale. A skilled potential partner makes sure the system remains reliable and performant as it grows.
• Security: This technology often carries security risks, stemming from both external cyberattacks and unintended actions by agents. Around 80% of organizations have reported experiencing risky behavior from AI agents, underscoring the importance of security. Companies need to ensure their artificial intelligence implementation partners understand security risks and are capable of handling them.

Conclusion

Companies are increasingly planning to deploy agentic artificial intelligence across their systems. They help automate complex tasks, allowing them to be carried out with minimal supervision. This improves productivity and helps accomplish tasks more efficiently.

However, implementing AI agents poses several challenges. Most companies lack the expertise to build, deploy, and scale automated agents in-house. Hence, they need to rely on agentic AI companies that function as implementation partners.

Businesses planning to explore AI implementation can refer to platforms like Techreviewer, which provide rankings and insights on firms that provide consulting and automation services. Such resources can help choose partners with the expertise required for effective agentic AI implementation.