THE REAL TEST OF AI AGENTS
Many people now know how to build AI agents, but very few trust them for real customer interactions and large-scale operations. Traigent.ai believes that the challenge lies not in the AI model itself but in the development stages that have yet to be fully addressed. "If you can measure it, we can optimize it.," says the company.
In recent years, technology companies have increasingly sought to place Generative AI at the heart of their products. On paper, the promise is compelling: AI agents that manage customer interactions, write code or analyze financial reports autonomously. In practice, however, they often fail to deliver.
Some 30 percent of generative AI projects will be abandoned after completing the initial proof of concept, according to the leading research firm Gartner. An MIT study goes even further: it finds that 95 percent of pilots fail because they rely on generic tools that perform well in demos but falter as adoption scales up. S&P Global adds another layer, noting that cost and reliability are the main barriers that prevent organizations from deploying effective AI agents. Importantly, the problem is not the models themselves. It is a deep engineering gap that becomes evident when systems move from the lab into real-world environments.
AI Isn't Trusted in Production
"During POC [proof of concept], everything looks promising," says Achi Solomon, Traigent.ai's CEO and co-founder. "You build a demo, present it, and it works perfectly on a small set of examples. Problems begin when the system is moved into production and encounters thousands of real users and countless unexpected scenarios. Suddenly, the system starts 'hallucinating,' response times increase, and costs skyrocket. It's at this stage that projects fail to realize their potential."
Understanding why trusting an AI agent is so difficult requires recognizing a paradigm shift in the software world. Traditional engineers are used to environments where a specific command consistently produces a predictable result. Generative AI, however, operates in a probabilistic space that is inherently less predictable.
"When you work with LLMs [large language models], you introduce an element of uncertainty into the system," says Dr. Nimrod Busany, Traigent.ai's CTO and co-founder. "The model may deliver an excellent answer today and an incorrect one tomorrow, simply because of a slight change in the instructions or information it was given."
The Lost Time of AI Developers
Attempting to solve these challenges manually is what causes many companies to fail. Developers can spend weeks on trial and error, juggling different tools and manually reviewing outputs. "Companies try to 'guess' their way to success," explains Busany. "They don't know the optimal configuration for their agent, and they lack the tools to evaluate its performance over time, quantitatively and accurately."
While many tools can measure and monitor AI agents, the missing capability is improving agents. This gap has given rise to a new category of solutions known as Continuous Tuning Infrastructure, which aims to provide developers not only with the ability to monitor performance, but also to continuously optimize their AI agents with the same rigor used in managing code.
This is where Traigent.ai comes in: .its unique infrastructure for automated, data-driven tuning of AI agents dramatically reduces time across the entire agent lifecycle.
Understanding the Real Return
Traigent.ai's vision is built on a unique blend of academic expertise and hands-on industry experience. Nimrod Busany previously led research labs at IBM and Accenture, has published over 20 academic papers and registered several AI and software engineering patents. Achi Solomon managed complex technology platforms at software, cybersecurity, and e-commerce companies such as Verint, Check Point, and Yotpo.
"We're at a turning point with AI, much like the early 1990s were for the internet," says Solomon. "For the revolution to succeed, however, AI engineers need reliable infrastructure, not just to write the initial code but to manage the AI agent's entire lifecycle."
Traigent.ai's platform lets AI engineers define any business KPI (accuracy, cost, latency, or other metrics) and automatically optimize the AI agent's configuration to best achieve those targets.
. "We're building a bridge that allows business teams to understand the real return on their investment, while giving technical teams the tools to reach those goals in a fast, engineering-driven, and measurable way," explains Busany. "Our motto is: 'If you can measure it, we can optimize it.'"
Traigent.ai collaborates with leading AI-focused companies such as Cloudzone.io, iFor.AI, Profisea.com, Bazak.ai, Comm-IT.com, Yotpo.com, and other notable industry players. It is also a partner in programs such as NVIDIA Inception and AWS Founders Club, and its research has earned international recognition both in the MLOps communities and at the CAIN 2026 International Conference on AI Engineering in Rio de Janeiro.
Finding the Balance Point
The central challenge in developing intelligent agents is identifying the 'balance point' between competing metrics, such as accuracy, response time, and cost. A system designed primarily for high accuracy may rely on a slower and more expensive model, but if faster responses or lower costs are required, accuracy can suffer. While these metrics are relatively straightforward, organizations may define additional success criteria and determine the optimal balance for each.
"Our system analyzes thousands of possibilities and provides developers with a precise, faster, and more cost-effective solution tailored to the defined success metrics , which can turn a failing project into a successful one", says Dr. Busany.
This capability is critical at a time when companies face challenges in assessing both the quality and economic efficiency of intelligent agents, he continues. "An AI project where each query costs only a few cents can quickly become unprofitable as user numbers grow." As a result, the process must not only involve selecting the right model but also continuously calibrating performance throughout the agent's lifecycle, ensuring alignment with the organization's business objectives.
The Future of Agents: Reliability as a Prerequisite
As the industry moves toward AI agents that not only answer questions but also take real-world actions (accessing bank accounts, booking flights, managing inventory), the demand for reliability increases dramatically. An agent that makes an informational mistake is problematic. An agent that errs in a financial transaction poses a real risk.
"In such a world, companies won't be able to release AI agents to market without a robust layer of evaluation and optimization," concludes Dr. Busany. "User trust in a digital agent depends on the ability to prove that it operates consistently and accurately within clearly defined boundaries."
In an era where every technology investment is judged by efficiency and performance, AI agents will also be held to clear and evolving standards. The question is no longer whether organizations will adopt these agents, but how they can ensure that they truly earn user trust ,— and that's precisely what Traigent.ai's infrastructure provides.
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What Does the System Actually Do?
Traigent.ai's solution tackles non-determinism, which is the central challenge of large language models (LLMs). Unlike traditional software, AI operates in a probabilistic space where even small changes in a prompt or in a version update can entirely alter the outcome. The company's infrastructure tests and measures variations across all parameters, —including models, prompts, and context,— enabling organizations to move from blind guesswork to structured control, evaluation, and continuous optimization of their AI agents.
In collaboration with Traigent.ai
