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Institutions that want to build and expand the agents also need to adopt another fact: agents are not designed like other programs.
The agents are “categorically different” in how they built them, how they work, and how it has improved, according to author CEO and co -founder Maybib. This means getting rid of the life development cycle of traditional software when dealing with adaptive systems.
Habib said on Wednesday during the theater Vb converting. “It is driven by the results. It is explained. It adapts. The behavior appears only in the environments of the real world.”
Knowing what succeeds-and does not succeed-comes from the experience of Habib to help hundreds of institutions agents to build and expand the scope of agents at the level of institutions. According to Habib, more than 350 of Fortune 1000 are customers from the writer, and more than half of Fortune 500 will provide scaling factors with the writer by the end of 2025.
Habib said that the use of unspecified technology to produce strong outputs could be a “truly nightmare”-especially when trying to expand the agents systematically. Even if the institutions teams are able to rotate the agents without the product managers and designers, Habib believes that the “PM Mind” is still necessary for cooperation, construction, repetition and maintenance.
“Unfortunately or fortunately, depending on your point of view, it will be left to carry the bag if their commercial counterparts do not lead to this new method of construction.”
>>Watch every coverage of our conversion 2025 here<Why are the agents based on goals is the right approach
One of the transformations of thinking includes an understanding of the results based on factors. For example, she said that many customers ask agents to help their legal teams in reviewing or re -linking contracts. But this is very open. Instead, the approach directed towards the target means designing an agent to reduce the time spent and re -link the contracts.
Habib said: “In the life cycle of traditional software development, it designs an inevitable set of very expected steps,” Habib said. “It is inputs, inputs in a more inevitable way. But with agents, you seek to form the agent’s behavior. So you are looking for a less controlled and much more flowing flow to give context and direct decisions by the agent.”
Another difference is building a plan for the agents who guides them with the logic of work, rather than providing them by following the workflow. This includes designing episodes of thinking and cooperation with subject experts to set operations that enhance the required behaviors.
While there is a lot of talking about scaling agents, the writer still helps most customers build them one after another. This is because it is important first to answer questions about who possesses and reviews the agent, who makes sure that he remains relevant and is still verified whether it is still producing the desired results.
Habib said: “There is a scaling cliff that people call very quickly without following a new approach to building the scaling agents.” “There is a cliff that people will get when their organization’s ability to manage the agents exceed the responsibility of the development that occurs by the department.”
Quality guarantee for agents for programs
Quality guarantee is also different from agents. Instead of an objective reference menu, the agent evaluation includes a non -bilateral behavior account and evaluating how agents work in the positions of the real world. This is because failure is not always clear – and not in black and white like checking something out. Instead, Habib said it is better to verify whether the worker behaves well, and asks whether the failure is working, and evaluating the results and the intention: “The goal here is not perfection is behavioral confidence, because there is a lot of self in this here.”
Habib said that companies that do not understand the importance of repetition in the end play “a fixed tennis game that wears every side so that you do not want to play anymore.” It is also important for the difference to be fine, with agents less than perfection and more about “launch them safely and quickly operating and repeating over and over again.”
Despite the challenges, there are examples of artificial intelligence agents who already help in achieving new revenues for institutions companies. For example, Habib, the main bank, who cooperated with the writer, mentioned the development of the agent, which led to a new pipeline of $ 600 million by operating new customers in multiple production lines.
New version regulations for artificial intelligence agents
The agent maintenance is also different. The maintenance of traditional software includes checking the symbol when something collapses, but Habib said that artificial intelligence agents need a new type of release control of everything that can form behavior. It also requires appropriate governance and ensuring that agents remain beneficial over time, instead of incurring unnecessary costs.
Since the models are not clean for artificial intelligence factors, Habib said that maintenance includes verification of claims, models settings, tool plans and memory composition. This also means that the executions are fully affiliated with inputs, outputs, thinking steps, tools and human interactions.
Habib said: “You can update a wave (a large language model) and watch the agent acts completely differently although anything in the history of Git has already changed,” said Habib. “Form’s links, update of retrieval indexes, the development of applications programming facades for tools, and suddenly do not behave the same router as expected … we can feel that we make ghosts.”
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