Artificial Intelligence Agents and Trust: How Trust in Enterprise AI Drives Business Growth

Today, the big innovation in enterprise artificial intelligence technology is agentic AI: artificial intelligence systems that act autonomously without constant human oversight, allowing them to achieve goals through decision-making, planning, and active execution. In other words, agentic AI is the key to the type of full automation promised by the AI and machine learning revolution.

Ultimately, if agentic AI technology is to succeed in the world of business, there must be one key element: trust. After all, the nature of agentic AI models is that they are (at least mostly) autonomous, performing functions and workflows with little to no human oversight. If business leaders cannot trust these AI systems, whether from an accuracy standpoint, a data security standpoint, or an efficiency standpoint, this is the point at which the technology becomes no longer useful.

Why trust in enterprise AI platforms is often lacking

One company working hard to bridge the gap between the autonomy of agentic AI tools and their quality is Krazimo, an enterprise AI solutions provider that helps companies leverage advancements in generative AI to boost employee productivity and supercharge growth. At the helm of the company is CEO and founder Akhil Verghese, who has seen firsthand why trust is so vital for businesses that want to use AI to their advantage.

Verghese points to the disparity between demo builds and the actual agentic AI products being sold to businesses as a prime example of why trust in agentic AI technology is at such a low level.

“AI has made it extremely easy to create a cool demo, but the gap between that product and something you'd actually trust to make consequential decisions within your business is vast,” Verghese explains. “In a rush to capitalize on VC interest and corporate AI budgets, many companies are selling slapdash agents that aren't ready for enterprise environments.”

How businesses can implement enterprise AI solutions while mitigating risk

However, this just scratches the surface of the trust issues surrounding agentic AI. Risks such as unpredictability, security vulnerabilities, and a lack of transparency must be managed by businesses that hope to integrate the technology into their workflows.

Of course, many involved in developing agentic AI products are hard at work creating solutions that address these concerns. However, as Verghese noted, not all AI agents are created equal.

If a business is looking to pursue agentic AI, it is crucial to look for providers who exhibit the following qualities:

• Determinism: “Agents often execute a series of steps,” explains Verghese. “Very often, an LLM can one-shot multiple steps, but this leads to software that's unpredictable and hard to debug. By breaking a workflow into a series of steps, it's possible to use standard software development on the steps where it suffices and, for the individual non-deterministic steps, use separate, individually testable agents. This modularity makes the overall workflow much more reliable and easier to maintain.”

• Testing: “The subjective nature of LLM outputs has led many AI engineers to abandon unit testing and functional testing altogether,” Verghese says. “This is a huge mistake — while the approach has to change, testing is still crucial. AI testing takes on a variety of different forms, from unit testing on the general theme of an output to using a second LLM to reflect on the answer of the agent.”

• Phased Launches: “I'd always recommend deploying AI agents in stages,” Verghese says. “Typically, you begin with a shadow launch, where it performs the task in the background, but a human still does the work. The results are compared to ensure adequate accuracy. The next step is often a ‘man in the middle’ launch, where the agent does the work, but requires human validation. Only when the results of this phase match human performance over a reasonable period of time should full automation be considered.”

“In general, agents deployed with the ‘man in the middle’ launch pattern are a trustworthy solution,” Verghese adds. “If the validation step involves no edits over a significant time, I'd consider that agent trustworthy. The only exception I can think of that definitely would require more scrutiny is areas where decisions are subjective and a wrong idea might lead the validator down a rabbit hole, such as diagnostic medicine.”

The future growth of artificial intelligence in enterprise use cases

Indeed, as we move into the future of agentic AI, the focus of innovators will shift from finding new use cases for the technology to improving those use cases that have already proven effective and useful. In this way, we can mitigate or even outright eliminate some of the concerns that critics of artificial intelligence have levied against agentic AI, including algorithmic bias, hallucinations and inaccuracies, and the loss of control.

“I think LLMs will get better and hallucinate less — we have clear evidence of that trajectory,” says Verghese. “But I think there's substantial room for growth in the area of agent-building best practices and tools. There are already a number of areas where agents, if built properly, can significantly reduce manual work in an enterprise setting with the accuracy of a competent human. I think it's about making those agents easier to build.”