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Enterprises that want to build and scale agents also need to embrace another reality: agents aren’t built like other software.
Agents are “categorically different” in how they’re built, how they operate, and how they’re improved, according to Writer CEO and co-founder May Habib. This means ditching the traditional software development life cycle when dealing with adaptive systems.
“Agents don’t reliably follow rules,” Habib said on Wednesday while on stage at VB Transform. “They are outcome-driven. They interpret. They adapt. And the behavior really only emerges in real-world environments.”
Knowing what works — and what doesn’t work — comes from Habib’s experience helping hundreds of enterprise clients build and scale enterprise-grade agents. According to Habib, more than 350 of the Fortune 1000 are Writer customers, and more than half of the Fortune 500 will be scaling agents with Writer by the end of 2025.
Using non-deterministic tech to produce powerful outputs can even be “really nightmarish,” Habib said — especially when trying to scale agents systemically. Even if enterprise teams can spin up agents without product managers and designers, Habib thinks a “PM mindset” is still needed for collaborating, building, iterating and maintaining agents.
“Unfortunately or fortunately, depending on your perspective, IT is going to be left holding the bag if they don’t lead their business counterparts into that new way of building.”
>>See all our Transform 2025 coverage here<<Why goal-based agents is the right approach
One of the shifts in thinking includes understanding the outcome-based nature of agents. For example, she said that many customers request agents to assist their legal teams in reviewing or redlining contracts. But that’s too open-ended. Instead, a goal-oriented approach means designing an agent to reduce the time spent reviewing and redlining contracts.
“In the traditional software development life cycle, you are designing for a deterministic set of very predictable steps,” Habib said. “It’s input in, input out in a more deterministic way. But with agents, you’re seeking to shape agentic behavior. So you are seeking less of a controlled flow and much more to give context and guide decision-making by the agent.”
Another difference is building a blueprint for agents that instructs them with business logic, rather than providing them with workflows to follow. This includes designing reasoning loops and collaborating with subject experts to map processes that promote desired behaviors.
While there’s a lot of talk about scaling agents, Writer is still helping most clients with building them one at a time. That’s because it’s important first to answer questions about who owns and audits the agent, who makes sure it stays relevant and still checks if it’s still producing desired outcomes.
“There is a scaling cliff that folks get to very, very quickly without a new approach to building and scaling agents,” Habib said. “There is a cliff that folks are going to get to when their organization’s ability to manage agents responsibly really outstrips the pace of development happening department by department.”
QA for agents vs software
Quality assurance is also different for agents. Instead of an objective checklist, agentic evaluation includes accounting for non-binary behavior and assessing how agents act in real-world situations. That’s because failure isn’t always obvious — and not as black and white as checking if something broke. Instead, Habib said it’s better to check if an agent behaved well, asking if fail-safes worked, evaluating outcomes and intent: “The goal here isn’t perfection It is behavioral confidence, because there is a lot of subjectivity in this here.”
Businesses that don’t understand the importance of iteration end up playing “a constant game of tennis that just wears down each side until they don’t want to play anymore,” Habib said. It’s also important for teams to be okay with agents being less than perfect and more about “launching them safely and running fast and iterating over and over and over.”
Despite the challenges, there are examples of AI agents already helping bring in new revenue for enterprise businesses. For example, Habib mentioned a major bank that collaborated with Writer to develop an agent-based system, resulting in a new upsell pipeline worth $600 million by onboarding new customers into multiple product lines.
New version controls for AI agents
Agentic maintenance is also different. Traditional software maintenance involves checking the code when something breaks, but Habib said AI agents require a new kind of version control for everything that can shape behavior. It also requires proper governance and ensuring that agents remain useful over time, rather than incurring unnecessary costs.
Because models don’t map cleanly to AI agents, Habib said maintenance includes checking prompts, model settings, tool schemas and memory configuration. It also means fully tracing executions across inputs, outputs, reasoning steps, tool calls and human interactions.
“You can update a [large language model] LLM prompt and watch the agent behave completely differently even though nothing in the git history actually changed,” Habib said. “The model links shift, retrieval indexes get updated, tool APIs evolve and suddenly the same prompt does not behave as expected…It can feel like we are debugging ghosts.”
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