“We Can Just Build It With Claude.” But Should You? What It Really Takes to Build Enterprise AI Agents

Building an AI agent with Claude, and other LLM’s has become remarkably accessible. What once required weeks of development can now take hours. But as organizations accelerate their AI initiatives, a more consequential question is emerging: Just because you can build an AI agent, should you?

That question anchored a recent Botco.ai webinar featuring Robert Alvarado, SVP of Information Management & Technology Services at Chicanos Por La Causa (CPLC); Rebecca Clyde, CEO of Botco.ai; Anu Shukla, Executive Chair of Botco.ai; and Chris Maeda, CTO of Botco.ai.

Their discussion challenged a common assumption about enterprise AI: that faster development automatically translates into faster business value.

The reality is more complicated. An AI prototype can demonstrate what is possible. An enterprise AI agent must demonstrate what is reliable, secure, maintainable, and valuable over time.

1. The Prototype Is the Easy Part. Production Readiness Is the Real Challenge.

AI coding assistants have dramatically reduced the effort required to develop functional software. But generating code is only one component of deploying a dependable enterprise system.

Chris Maeda described the shift in development economics:

“Now the bottleneck is review and quality testing.”
Chris Maeda, CTO, Botco.ai

As code generation accelerates, organizations must devote greater attention to testing, security reviews, scalability, and operational reliability.

Anu Shukla shared a practical example from Botco.ai’s own product development experience. After the team developed an SMS engagement capability, customers requested additional features, including geographic information and ZIP code identification.

Those requirements weren’t fully apparent during initial development. Incorporating them required additional engineering work.

“There’s a lot more to it to really finish it up.”
Anu Shukla, Executive Chair, Botco.ai

The lesson is that a prototype typically demonstrates a well-defined scenario. Production systems must accommodate changing requirements, unexpected user behavior, and operational exceptions.

Rebecca Clyde emphasized the importance of designing for those exceptions:

“Is every interaction being documented in such a way that you can review it later?”
Rebecca Clyde, CEO, Botco.ai

That question goes to the heart of enterprise readiness. An AI agent must not only complete tasks. Its behavior must also be observable, auditable, and accountable.

2. Your AI Agent Is Only as Reliable as Your Data and Processes

One of the most revealing observations came from Robert Alvarado, whose organization operates across seven states, with approximately 51 lines of business and 132 locations.

For CPLC, AI deployment involves coordinating diverse services, operational requirements, and information systems.

Alvarado explained that the greatest implementation challenges often arise before the AI itself is developed.

“Everything is built off of your data and processes.”
Robert Alvarado, SVP, CPLC

When processes are clearly documented and data is reliable, automation becomes substantially easier. When processes are inconsistent or constantly changing, AI can amplify existing operational problems.

Rebecca Clyde made the same point:

“If your business process is still broken … the AI is just gonna make that go faster.”
Rebecca Clyde

Consider an AI agent designed to help customers navigate eligibility requirements. If the underlying policies are outdated, the agent may confidently communicate information that is no longer valid.

Similarly, an agent connected to multiple internal systems needs a clearly established source of truth. Otherwise, conflicting records can produce inconsistent responses.

The takeaway: Before investing in AI development, organizations should evaluate the quality of their data, the maturity of their workflows, and who owns the information the agent will use.

3. Security, Compliance, and Human Oversight Cannot Be Afterthoughts

Enterprise AI agents increasingly interact with sensitive information and business-critical workflows. This makes governance an architectural requirement rather than a final implementation checklist.

For CPLC, which operates programs involving personally identifiable information and HIPAA-related requirements, security considerations influence technology decisions from the beginning.

Alvarado described using minimum requirements, NIST guidance, sandbox testing, and reviews by people outside the development team.

Crucially, purchasing a solution does not eliminate the organization’s responsibility to oversee it.

“That doesn’t transfer the accountability to the vendor.”
Robert Alvarado

Anu Shukla also stressed the importance of establishing organizational AI policies, particularly when employees use general-purpose AI tools with potentially sensitive company information.

“If you’re going to make these tools available to your employees, you have to put those in place.”
Anu Shukla

Effective governance includes controlling what information agents can access, what actions they can execute, and when human approval is required.

The panel outlined seven essential layers of enterprise AI architecture: data, integrations, workflows, permissions, guardrails, monitoring, and human handoff.

Together, these components determine whether an agent can operate safely within a real business environment.

4. The Total Cost of AI Ownership Extends Far Beyond Development

The apparent affordability of building with Claude can obscure the ongoing cost of operating an AI agent.

Development is only the initial investment. Organizations must also account for integrations, infrastructure, testing, monitoring, troubleshooting, model changes, usage costs, and the employees responsible for maintaining the system.

Maeda offered a useful analogy:

“It’s like you get a developer that types really fast.”
Chris Maeda

That faster developer does not eliminate the need for experienced engineering judgment or operational infrastructure.

The panel also discussed unpredictable AI usage costs. Maeda shared that experimenting with an automated coding workflow that consumed so many tokens within days the organization had to shut it down.

The example illustrates why organizations must understand both the technical architecture and the economics of the tools they deploy.

A more complete total cost of ownership calculation should examine:

• Initial development and implementation
• Integration with existing enterprise systems
• Security, compliance, and quality assurance
• Ongoing monitoring, maintenance, and upgrades
• Infrastructure, model usage, and scaling
• Internal staffing and long-term support

The key question is not simply how much an AI agent costs to build. It is how much the organization must invest to keep that agent reliable and useful.

5. Build vs. Buy: The Right Answer Depends on the Business Problem

Perhaps the most compelling part of the discussion came from Alvarado’s firsthand experience at CPLC.

He described a department that purchased an off-the-shelf solution without initially involving IT. Once implemented, the product reportedly met only about 20% of the department’s needs.

CPLC’s technology team subsequently developed an internal alternative using a SharePoint backend and a Claude-assisted frontend. The customized solution addressed the department’s requirements and eliminated approximately $25,000 in annual software spending.

But Alvarado also described the opposite scenario.

For another project, a required certification would have taken an estimated six to nine months to obtain internally, with significant additional resources. Purchasing an existing compliant solution made more sense.

These examples demonstrate why build versus buy cannot be reduced to a universal rule.

Maeda offered his own preference for established solutions:

“You’d rather have mature solutions with a bigger user base than one-offs that you build yourself.”
Chris Maeda

The practical framework is to build when customization provides meaningful value and the organization can support the solution; buy when established capabilities, compliance requirements, or speed to deployment favor an experienced provider; and combine both when a hybrid approach best fits existing systems.

6. The Real Measure of Enterprise AI Is Business Value

A functioning AI agent is not necessarily a successful AI initiative.

Organizations need to determine whether their agents improve operational efficiency, resolve customer needs, reduce unnecessary work, or deliver other measurable outcomes.

That requires defining success before deployment and evaluating performance afterward.

Clyde summarized the distinction:

“Product readiness is really a system, not a prototype.”
Rebecca Clyde

Enterprise AI requires coordination among technology, security, operations, compliance, and business stakeholders. No single tool or developer can substitute for that collective expertise.

The Bottom Line: Build for Outcomes, Not Just Speed

Claude and other AI development tools have fundamentally changed what organizations can create and how quickly they can create it.

But enterprise readiness still requires reliable data, well-defined processes, secure integrations, enforceable guardrails, ongoing monitoring, and clear accountability.

The most effective AI strategy is not necessarily to build everything internally or purchase every capability from a vendor. It is to understand where internal development creates differentiation, where established platforms reduce risk, and how each investment contributes to business outcomes.

The question has shifted from “Can we build it?” to “Can we operate it responsibly, maintain it economically, and prove its value?”

That is the decision enterprise leaders ultimately need to make.

Watch the on-demand webinar, “We Can Just Build It With Claude.” But Should You?, to hear the full discussion with Robert Alvarado, Rebecca Clyde, Anu Shukla, and Chris Maeda.

Frequently Asked Questions

What is the difference between an AI prototype and an enterprise AI agent?

An AI prototype demonstrates a capability in a limited environment. An enterprise AI agent must also support secure integrations, permissions, monitoring, exception handling, human escalation, and ongoing maintenance.

When should an organization build an AI agent instead of buying one?

Building may be appropriate when specialized requirements justify customization and the organization has the resources to maintain the solution. Buying may be preferable when an established platform can satisfy security, compliance, integration, and operational needs more efficiently.

What are the biggest hidden costs of enterprise AI agents?

Commonly underestimated costs include integration work, testing, security reviews, model usage, monitoring, maintenance, infrastructure, and the staff required to oversee AI performance.