Most teams building AI agents focus on the wrong milestone. They hit production, leadership is thrilled, and everyone assumes the hard part is over. Then reality sets in: customer needs evolve, new edge cases emerge, more channels get added, and suddenly you realize the agent is a living system that needs constant investment.

Behind every workflow update also lies the complex technical burden of fine-tuning models, optimizing latency, maximizing reliability, evaluating new providers, and implementing safety guardrails. But the deeper challenge is that none of the engineering work stops.

This is where teams face the classic build-vs-buy dilemma, but with a twist. The calculus that works for traditional software breaks down for AI agents.

Why some companies choose to build in-house

Building internally is often a rational starting point, especially when you can spin up a working demo quickly. Your engineers are already on the team, cloud infrastructure is already provisioned, and open-source models are commoditizing fast. You save on third-party spend while maintaining control over workflows and business logic.

But there's a hidden assumption: that an AI agent is like traditional software, where you build it once, maintain it occasionally, and mostly let it run. Unlike applications that are largely stable once deployed, your AI agent needs to learn new procedures every week, handle emerging customer issues in real-time, expand to new use cases, and adapt as your business evolves.

What looks like a reasonable project during scoping becomes a permanent allocation of engineering resources. The question isn't whether your team can build it, but whether this is where you want your capacity invested long-term.

The real cost of building internally

The real costs of developing an AI agent don’t show up in early demos, but emerge in the engineering cycles you must dedicate indefinitely, the performance gaps that compound over time, the operational risks you absorb, and the infrastructure you have to maintain.

Engineering time

Production agents require workflow design, RAG pipelines, evaluation frameworks, safety layers, latency optimization, analytics instrumentation, security controls, prompt updates, model migrations, and ongoing monitoring.

This isn't one-time work. Every new customer issue requires prompt updates. Every new channel needs integration work. Every model upgrade requires migration and testing.

The opportunity cost is substantial, as time burned on infrastructure is time not spent improving your core product.

Performance gaps

Even when an agent "works", performance degrades as customer needs shift and edge cases accumulate. Latency spikes, hallucinations, inconsistent behavior, and subtle accuracy degradations also emerge continuously.

At scale, these performance gaps are very expensive. Poor customer experiences hurt the business in significant ways, even if they’re difficult to precisely quantify.

Operational risks

Owning the system means owning the on-call rotations, incident response, failover architecture, version drift across LLM providers, telephony reliability, and integrations that can break unexpectedly. These risks show up not as clean budget items but as outages, churned users, engineering burnout, and slower iteration cycles.

Infrastructure

Building an AI agent quickly expands into a large, interdependent infrastructure footprint. GPU orchestration alone requires capacity planning, spot instance management, failover logic, and real-time cost optimization—before you've written a single line of agent logic.

Production systems also need model inference, vector databases, embedding workflows, multi-region redundancy, telephony for voice use cases, and observability tooling. Each component seems manageable in isolation, but together they form a distributed system that demands constant coordination and escalating costs.

Where teams should maintain control

Not every component of an AI agent contributes equally to competitive differentiation. Enterprises should focus on layers that directly influence customer outcomes: workflows, domain logic, brand voice, escalation rules, and proprietary internal tools. These define how well your agent represents your brand and delivers concierge-level experiences. Critically, these are things that change very often.

But the deeper you go into the stack, the less strategic that ownership becomes. Model evaluation and experimentation, agent orchestration, RAG pipelines, latency and reliability engineering, telephony infrastructure, and disaster recovery are necessary for any production-grade agent, but perfecting them offers no unique market advantage. Maintaining them creates friction that slows the iteration that actually matters.

Offloading these tasks reduces overhead but doesn't reduce control. It frees engineering teams from maintaining the infrastructure that customers never see and competitors also must build.

Why many vendors make this (much) harder

Many vendors approach AI agents like traditional SaaS: they deploy teams of engineers to customize and implement for you. This heavy-services model can deliver initial results, but it creates a black box that becomes very expensive and difficult to modify over time. Worse, the teams defining your workflows aren't the ones implementing them, making it painful to validate that everything works as intended.

For AI agents specifically, this is the wrong architecture. You need to iterate daily, but service-heavy models make that structurally impossible. Instead of enabling autonomy, these constraints trap teams in a vendor-mediated process where every update requires submitting a support ticket and waiting on their backlog.

As you scale, the logic you need to teach your AI multiplies. Without the ability to make changes yourself, costs balloon and you're locked in. What should simplify complexity ends up adding another layer of it, increasing friction and undermining the very reason to buy rather than build.

Reframing the equation

The ideal solution gives companies control where it matters while removing the operational burden that doesn't.

This requires a product-first approach rather than a services-first one. CX, product, and engineering users should get hands-on partnership throughout the agent development lifecycle, but shouldn’t need to rely on vendor resources for every iteration and change.

Decagon is built around that principle. We put everything into the product so your team has direct control over your AI agent and visibility into its performance:

• Agent Operating Procedures (AOPs) make it easy to build, adjust, and evolve workflows in natural language
• Simulations and unit tests validate logic before anything reaches production
• Agent Versioning enables controlled roll outs and A/B testing to safely experiment with updates
• Watchtower and the analytics suite surface insights from customer conversations that accelerate hillclimbing
• Diagnostic Tools and Trace View help debug issues and quickly identity opportunities to improve performance

Teams can iterate and ship faster than teams locked into sprint cycles. Over time, that gap becomes insurmountable. Your agent gets smarter, handles more use cases beyond reactive support, and delivers better experiences while competitors wait for vendor tickets to be prioritized.

Making the right choice

If you're weighing build-vs-buy, don't just ask whether you can reach production. Ask whether you can evolve continuously. Can your team update workflows weekly without engineering sprints? Can you expand to new channels without rebuilding infrastructure? Can you tune performance in real-time as issues emerge?

The right answer depends on your specific architecture, team composition, and business requirements. But the framework remains consistent: own what differentiates you, automate what doesn't, and optimize for iteration velocity above all else.

Reach out for a demo to see how Decagon can help you move faster.