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Businesses want to build and scale agents should also accept another reality: agents are not built like other software.
The “category of race” agents how they build, how they act, and how they progressed, according to WRITER CEO and co-founder have Habib. This means united with the traditional life development cycle in dealing with adaptive systems.
“Agents are not reliable following the rules,” said Habib on Wednesday while on the stage of Change in VB. “They transferred the consequences. They interpret. They adapted. And the behavior really emerges in the world’s surroundings.”
Knowing what works – and what doesn’t work – comes from Habib’s experience to help hundreds of business clients build and measure business agents. Habib said the writer’s customers were over 350 in Fortune 1000, and more than half of the hundred 500 can scale agents at the end of 2025.
The use of unpolerable tech to produce powerful outputs can be “Guide,” said Habib – especially when trying to scale systems systematically. Although business teams can waste agents with no product managers and designers, Habib thinks a “pm of mind” that still needs to work together, build and maintain agents.
“Unfortunately or in sorrow, depending on your view, it will leave the bag hold if they are not led by the new construction method.”
>>See all our change 2025 scope here<Why objective agents are the right method
One of the mental transfers includes the understanding of nature based on the result of agents. For example, he says many customers ask for agents to request their legal teams to review or redlining contracts. But that’s already open. However, a goal-oriented method means designing an agent to reduce the time allocated to review and redlining contracts.
“In the traditional life development cycle in life, you designed for a deterministic set of clear measures,” Habib said. “This is the input, input in a more deterministic way. But with agents, you seek to count agenic behavior.
Another difference is to build a plan for agents who teach them business logic, rather than to give it to workflows to follow. It includes designing the logic loops and collaborated with subject experts to map the processes that promote desirable behaviors.
While there is a lot of talk about scaling agents, the author still helps most clients build them each. That’s because it is important to answer the questions about who owns and the agent is audited, ensuring that it is relevant and produces preference yet.
“There is a scaling cliff that people get, very quickly without a new method of building and scaling agents,” Habib said. “There is a cliff that people who go to reach their organization’s ability to handle agents responsible for the actual retreat of department.”
QA for agents against software
The quality assurance of agents is also different. Instead of an objective checklist, the agent’s review includes accounting for non-binary behavior and evaluations of how agents act in real-world situations. That’s because failure is not always clear – and not as black and white as checking when something is broken. However, Habib said it would be better to check if an agent conducted greatly, asking for failures worked on purpose: “The purpose here is the whole opposition.”
Businesses that don’t understand the importance of healing to bloom to play “a constant tennis game just wearing each side until they don’t want to play,” Habib said. It is also important for teams to be okay with agents less perfect and more about “launching their safety and throw away.”
In spite of challenges, there are examples of AI agents who help bring new income for business businesses. Example, Habib mentions a major bank that helps the author to develop a agent-based system, resulting in a newly UPSell pipeline worth $ 600 million in many product lines.
The New Version Conference for AI agents
The agent’s agent’s agent is also different. Taking care of the traditional software involves examination of the code if something breaks, but Habib says a new version of version control. It also requires proper management and ensure that agents remain useful for hours, instead of having costs needed.
Because models cannot be cleared by AI agents, Habib said to check the prompts, model models and memory configuration. It also means to fully track the killings of inputs, outputs, logic steps, tool calls and human interaction.
“You can update a (large language model) LLM prompt and see the agent to change even if no one changes history,” Habib said. “Modeling Model Modifications, Updates Updates to Getting Taking, The Tool Apis Apolveve and suddenly the same prompts don’t work like debugging ghosts.”
