Buy vs Build AI Customer Support | Clear Framework

Buy vs Build: Using the CLEAR Framework to Evaluate AI Customer Support Agents

When implementing AI agents in existing organizations, there’s one question that comes up the most: ‘Should we build our own AI agents for X?’ or ‘Will we even need to buy anything at all in a world where anyone can build their own tools?’

We’ve developed a method to answer this question: the CLEAR framework, a practical tool to help you navigate when to build, when to buy, and how to make that decision with a bit more confidence.

The CLEAR framework

Confidence

Diane Greene (ex-VMWare CEO) famously said that “you should only build what you’re confident you can do better than anyone else”. That’s the essence of this dimension: based on your organization’s nature, do you truly have the confidence (in your talent, focus, and execution) to build AI agents that outperform off-the-shelf solutions?

Some organizations genuinely do have this expertise. Companies with strong AI teams, unique proprietary data, or workflows so specialized that no vendor addresses them may find building delivers better outcomes. The question is honest assessment: can you build something better than available alternatives, or are you overestimating internal capability?

Long Tail

Where is your long tail? Is it hidden in your core flows? In most systems or companies, the edge cases are the core. Teams tend to design and build for the happy path (often validated quickly with ChatGPT/Claude/Gemini), but in most cases, real value lies in handling the outliers. Task: Map your core flows, then ask: how many are driven by “IFs” and exceptions - that’s where your real complexity lives.

Consider first notice of loss intake for an insurance carrier. The happy path seems simple: customer reports claim, system captures details, claim routes to adjuster. A demo takes thirty minutes to build.

Then production reality hits. Coverage verification across policy types and jurisdictions. Determining whether the loss falls within policy period and territory. Identifying potential subrogation opportunities. Flagging fraud indicators that require special handling. Routing to different adjusters based on claim complexity, coverage type, and regulatory requirements. Coordinating document collection that varies by claim category.

The thirty-minute demo becomes six months of engineering, and you're still discovering edge cases a year into production.

Effort

Agentics workflows aren’t set-and-forget (at least not in the near future) as they’re living systems that need constant tuning, replacing and testing new models, updating prompts, maintaining integrations and data pipelines, QA, and so on. 

Building your own means owning the entire lifecycle: from prompt regressions to new product logic, knowledge, and data, escalations, and edge-case monitoring. Ask yourself: Who on your team will own this? Are you prepared to hire (or shift) people into roles like prompt engineer, AI QA, and agent product manager?

Affordability

Affordability isn’t just about what you pay, it’s about the total cost of ownership or changes, especially the cost of your team’s time, focus, and velocity.

Even if the models become (and they will) much cheaper and easier to use, the real costs for engineers, AI specialists, QA, product managers, software, and hardware will escalate quickly. Standard TCO calculations miss critical categories: discovery and design before development begins, integration development scaling with system count, ongoing inference costs that surprise teams accustomed to fixed infrastructure, and maintenance burden compounding over time.

Run the full calculation for your specific situation rather than defaulting to either assumption.

Real-Life Impact (or real-world mistakes)

Not all AI agents operate under the same spotlight. Some live quietly behind the scenes while others are exposed directly to customers' interactions, in moments that shape trust, loyalty, or frustration. The more public-facing your agent is, the higher the stakes, and the less room there is for error, delay, or wrong AI behavior.

Ask yourself: If this agent fails or makes a mistake, who notices? Your ops team, or your customers? And what’s the cost of that failure?

High-stakes, customer-facing applications demand robust guardrails regardless of build or buy. Building requires investing in safety engineering from scratch. Buying requires verifying that vendor guardrails meet your specific risk tolerance and compliance requirements.

Making the Right Choice for Your Business

To sum everything up, the decision to build or buy AI agents isn’t just technical - it’s strategic. The CLEAR framework helps unpack five critical dimensions: Confidence, Long Tail, Effort, Affordability, and Real-Life Impact. Each forces you to look beyond short-term convenience and ask what’s truly sustainable, differentiated, and worth owning. Before you build, get CLEAR on whether you should.

At Notch, we don't just automate flows - we own outcomes. Our platform combines structured workflows, policy controls, and real-time intelligence to deliver AI agents that operate autonomously, precisely, and safely across a wide range of industries. 

‍

We build production-ready agents for your teams that integrate with custom systems in minutes, enforce policies across brands and regions, automate customer interactions and workflows across tools, surface insights from every conversation, and keep behavior controllable through built-in rules. We don't sell tooling. We deliver full-stack AI agents that work - at scale.