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Enterprises who want to build and measure agents also need to adopt another fact: agents are not made like other software.
According to the agents are “clearly different” how they are made, how they walk, and how they improve. Writer CEO and co -founder May Habib. This means digging traditional software development life cycle while dealing with adaptive systems.
Habib said on Wednesday, “Agents do not trust the rules reliably. VB Transform. “They are based on the results.
Knowing what works-and does not work-provides hundreds of enterprise clients with the construction and scale of enterprise grade agents with Habib experience. According to Habib, more than 350 authors of Fortune 1000 are the authors, and more than half of Fortune 500 will be a scaling agent with the author by the end of 2025.
Habib said-especially when agents try to try a scale, the use of non-collision tech to develop a powerful output can also be a “really scary dream”. Even if the enterprise teams can rotate agents without product managers and designers, Habib believes that “the Prime Minister’s mentality” is still needed to cooperate, construct, repetition and maintain agents.
“Unfortunately or fortunately, depending on your point of view, if they do not take their business counterparts in this new style of the building, the bag is kept holding.”
>> See all our Transform 2025 coverage here <The round -based agent is why is the correct point of view
A change in thinking also includes understanding the nature of the results of the agents. For example, he said many users request agents to help their legal teams review or help their legal teams. But it is very open. Instead, a purpose -based approach means designing an agent to review contracts and reduce time spending time in red lining.
“In the traditional software development life cycle, you are designing for a predictable set,” said Habib. “This is input, the maximum input out. But with the agents, you are trying to form an agent. So you are looking for less control flow and more to guide the agent and guide the decision -making.”
Another difference is to make a blueprint for agents that instructs them from business logic, rather than providing them to follow the work flu. This includes arguing for reasoning and cooperating with topics for maps that promote the desired behavior.
Although there are a lot of talk about scaling agents, the author is still helping most clients build them at a time. The reason for this is that it is first important to answer questions about the agent owner and the audit, which ensures that it remains relevant and still checks whether it is still producing the desired results.
Habib said, “There is a scaling mountain that people arrive very quickly without a new approach to the building and scaling agents.” “There is a mountain in which people are approaching when the ability to manage their organization’s agents moves at the pace of development of the department by the department by the department.”
QA for agents vs Software
Quality assurance is also different for agents. Instead of an objective checklist, the agent’s diagnosis involves calculating non -binary behavior and guessing how the agents work in real -world conditions. The reason for this is that failure is not always clear – and not as black and white as something breaks down. Instead, Habib said it is better to test whether an agent behaves well, he asks whether the Fail Safez works, reviews the results and intentions: “The purpose here is not perfection, because it has a lot of subjectiveness here.”
Habib said, “The business that does not understand the importance of repetition” play a permanent game of tennis that only wear everywhere unless they want to play now. ” It is also important for teams to be less than the agent perfect and “more about launching them safely and moving fast and repeatedly repeated.
Despite the challenges, there are examples of AI agents that already help the enterprise business bring new income. For example, Habib mentioned a major bank that developed an agent -based system with the author, which resulted in new customers mounted on multiple product lines and created a new upsel pipeline worth $ 600 million.
Controls a new version for AI agents
Agent’s care is also different. Traditional software maintenance includes code checking when something breaks, but Habib said that AI agents need a new type of version control for everything that can create behavior. It also has to make proper governance and to ensure that agents are useful over time rather than lifting unnecessary expenses.
Since the model does not make a clean map for AI agents, Habib said that maintenance includes indicators, model settings, tool schemes and memory. This also means that the inputs, output, reasoning measures, tool calls and complete executions on human interaction.
Habib said, “You can update a (large language model) LLM prompt and see the agent completely differently, though nothing has changed in the history of the gut.” “Model links shift, recovered indexes are updated, tool APis is ready and suddenly the same indication does not behave as expected … It seems that we are debuting ghosts.”
