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Why Building AI Agents Is a New Paradigm for Enterprises
Enterprises looking to build and scale AI agents must first accept a fundamental truth: agents aren’t developed like traditional software.
According to May Habib, CEO and co-founder of Writer, agents are categorically different—in how they’re constructed, how they behave, and how they evolve. This demands a shift away from the conventional software development life cycle, especially when working with adaptive systems.
“Agents don’t reliably follow rules,” Habib explained on stage at VB Transform. “They are outcome-driven. They interpret. They adapt. Their true behavior only becomes apparent in real-world environments.”
Habib speaks from deep experience, having supported hundreds of enterprises in launching and scaling production-grade AI agents. Writer already counts over 350 Fortune 1000 companies as clients, and Habib predicts that by the end of 2025, over half of the Fortune 500 will be scaling agents using Writer’s platform.
But that journey isn’t easy. Habib cautions that using non-deterministic AI to deliver enterprise-ready outputs can be “a nightmare” without the right systems in place. Even if teams bypass traditional roles like product managers and designers, a PM-style mindset is still essential for collaboration, iteration, and agent lifecycle management.
“If IT leaders don’t guide their business counterparts into this new way of building, they’ll be the ones left holding the bag,” she warned.
Embracing a Goal-Based Approach to Agents
One key mental shift involves understanding that agents must be goal-oriented, not task-oriented. For instance, clients often request agents to assist legal teams with reviewing contracts. But the real objective isn’t just “reviewing”—it’s reducing the time spent on that process.
“Traditional software development is about predictable, linear flows,” Habib said. “With agents, you’re shaping behavior, not controlling steps. It’s about giving context and guiding decisions—not scripting them.”
This requires a different kind of blueprint. Rather than coding in step-by-step workflows, builders must define business logic, reasoning loops, and contextual instructions. Subject matter experts become collaborators in shaping how agents think, not just what they do.
Although there’s increasing buzz about scaling agents, Writer still focuses most client engagements on building one agent at a time. Why? Because foundational questions—like who audits the agent, ensures its relevance, or governs its outputs—must be resolved first.
“There’s a scaling cliff companies hit very quickly if they don’t rethink how agents are built and managed,” Habib said.
Why QA for Agents Isn’t Like QA for Software
Quality assurance for agents doesn’t follow a checklist. Instead, it requires evaluating real-world behavior—measuring how well agents achieve intended outcomes in fluid, unpredictable environments.
Failure isn’t binary. It’s not about something simply “breaking.” It’s about whether the agent acted responsibly, whether fail-safes were triggered, and if the final result aligns with the intent.
“We’re not aiming for perfection,” said Habib. “We’re aiming for behavioral confidence—a much more nuanced goal.”
Teams that fail to embrace this approach often fall into a frustrating cycle of reactive back-and-forth, stalling progress. Instead, success comes from safe launches, rapid iteration, and a tolerance for imperfection.
Real Enterprise Impact: Agents That Drive Revenue
Despite the challenges, enterprise agents are already delivering tangible results. Habib shared a compelling example: a major bank that worked with Writer to build an agent-based onboarding system. The result? A $600 million upsell pipeline, by introducing new customers to multiple product lines.
Rethinking Version Control and Maintenance for AI Agents
Maintaining agents also diverges sharply from traditional software upkeep. In legacy systems, version control is straightforward—code breaks, you fix the code. But with AI agents, changes in prompts, model settings, memory, or APIs can dramatically alter performance, even if no code was touched.
“You can tweak a prompt and the agent suddenly behaves in an entirely different way,” Habib said. “It’s like debugging ghosts—because model behavior shifts behind the scenes.”
Proper versioning, tracing, and governance are essential. Teams must track not just the code, but everything that influences an agent’s behavior: prompt templates, tool schemas, retrieval indexes, API responses, and more.
Final Takeaway
Building and scaling enterprise-grade agents isn’t just a technical challenge—it’s a fundamentally new way of thinking. From outcome-oriented design to adaptive QA and behavior-based versioning, enterprises must adopt new mindsets, methods, and strategies. As May Habib put it, the organizations that succeed will be those who lead this transformation—not just react to it.
Frequently Asked Questions
What is the “Hidden Scaling Trap” in agent deployments?
The hidden scaling trap refers to the challenges organizations face when they try to rapidly scale AI agents without rethinking their development, governance, and maintenance strategies. Traditional software approaches don’t work, and this mismatch can lead to failures, inefficiencies, or unpredictable agent behavior.
Why can’t agents be scaled like traditional software?
Agents are non-deterministic, context-aware systems that adapt and evolve. Unlike traditional software that follows fixed rules, agents rely on dynamic data, reasoning loops, and learning mechanisms, making standard software life cycles inadequate.
What are the early warning signs that my team is falling into the scaling trap?
Key signs include agents behaving inconsistently, difficulty debugging changes, no clear ownership model, lack of version control across prompts and configurations, and friction between departments during rollout or iteration.
What’s the biggest misconception about scaling AI agents?
Many believe that once a working agent is built, it can be replicated across departments. In reality, each agent needs context-specific logic, oversight, and continuous iteration to remain useful and aligned with business goals.
Who should own the responsibility of scaling agents in an enterprise?
IT leaders must take the lead but work collaboratively with business units, product owners, and compliance teams. Without cross-functional accountability, agents can drift from their intended outcomes.
How do you properly evaluate the performance of scaled agents?
Instead of using binary test cases, agent evaluation should be based on behavioral confidence, outcome achievement, real-world usage data, and whether fail-safes were triggered during unintended behaviors.
What is “behavioral confidence” in agent QA?
Behavioral confidence is a quality metric that assesses how consistently and appropriately an agent behaves in dynamic environments. It’s about ensuring reliability, safety, and alignment with intent—not perfection.
Why is traditional version control insufficient for agent systems?
Because agent behavior depends on more than just code. Prompts, retrieval indexes, tool APIs, and even slight model changes can cause unpredictable results, requiring new forms of traceability and behavioral versioning.
What’s the best approach to avoid the scaling trap?
Start small with a clear goal, involve stakeholders early, establish audit and feedback loops, create scalable governance frameworks, and design agents with adaptability and safe iteration in mind.
Can you scale agents without product managers or designers?
Technically yes, but strategically it’s risky. A product mindset is essential to define goals, measure impact, and ensure that agents stay aligned with business needs through multiple iterations.
How do you ensure an agent continues to produce value after deployment?
Through regular audits, prompt updates, memory tuning, behavior testing, and engagement with subject matter experts. Agents require ongoing care—not just post-launch maintenance.
What happens if you ignore the scaling trap?
Ignoring the trap can lead to wasted resources, poor user trust, rogue behavior, compliance risks, and eventually a breakdown in organizational confidence in AI initiatives.
Conclusion
The journey to deploying and scaling AI agents holds immense potential—but it’s also lined with hidden pitfalls. The biggest trap isn’t in building the first successful agent. It’s in assuming that what works for one will automatically scale across your organization. AI agents are fundamentally different from traditional software—they adapt, interpret, and evolve. Scaling them without rethinking governance, evaluation, and maintenance can lead to instability, inefficiency, and even failure.
To stay ahead, enterprises must adopt a goal-oriented mindset, invest in cross-functional collaboration, and implement robust version control and behavioral testing. This isn’t just a technical evolution—it’s an organizational shift. By acknowledging the complexity and embracing a new development paradigm, businesses can turn the hidden scaling trap into a strategic advantage and unlock the full power of AI agents at scale.
