Build vs. Buy AI Agents: Everything You Need to Know

With 94% of IT leaders planning to deploy autonomous agents in the next two years, and over $1.3 billion raised to build them, AI agents aren’t experiments anymore. They’re becoming core infrastructure, reshaping how businesses operate, make decisions, and scale impact.

This shift raises a high-stakes question for enterprise leaders: should you build proprietary agentic systems tailored to your environment, or buy off-the-shelf tools to move faster?

This isn’t a procurement question. It’s a strategic one, influenced by your delivery velocity, control over system behavior, and ability to align AI execution with business goals. In some cases, buying may provide short-term gains but create long-term limitations. In others, building might offer strategic control but delay time-to-value.

In this guide, we’ll discuss how to evaluate what your business really needs from agents, and how to make the right call for your roadmap, your teams, and your outcomes.

Before deciding whether to build or buy AI agents, organizations need to ask a more fundamental question: What role will agents actually play in advancing your business?

It starts with identifying where AI agents can drive real value. That might mean automating repetitive workflows, enhancing human decision-making, or acting autonomously within enterprise systems. In some cases, agents go further, creating entirely new capabilities like code generation or real-time operations across teams.

But knowing what’s possible doesn’t clarify what’s necessary. It’s important to understand how agents intersect with your specific business priorities.

1. Start with the problem, not the promise
   AI agents can deliver value across a wide range of workflows, from customer service to product development. But unless you’ve clearly defined what they’re solving, the technology won’t land.
   Ask:
   - What’s the exact task or process this agent will take on?
   - Is it core to how we differentiate, or just something we need done efficiently?
   - Will this agent augment human teams, act independently, or both?
   
   Whether you're optimizing underwriting decisions or enabling self-serve IT support, specificity is critical. The complexity of the use case directly influences how much customization, training, and integration the agent will require.
2. Align agent strategy to business value
   If the agent is core to what makes your business unique, like a custom recommendation engine or a trading algorithm, building it in-house may be worth the time and cost. But if it’s a common task, like handling FAQs or scheduling, buying an existing solution is often faster, cheaper, and good enough.
3. Assess your internal capacity and constraints
   This is where most build aspirations break down. Advanced AI agents aren’t just apps with a nice UI; they’re system-level constructs that rely on deep model tuning, continuous feedback loops, and robust infrastructure. If you don’t have internal teams that specialize in reinforcement learning, prompt tuning, or orchestration layers, building becomes slow, costly, and brittle.
   
   Additionally, it’s not just about centralized initiatives. As Ethan Mollick, Co-Director of the Generative AI Lab at Wharton, puts it:
   “Organizations are getting filled with secret cyborgs who are doing all their work with AI and not telling anyone. And if leaders don’t realize that, that is both a huge risk and a huge opportunity.”
   
   Shadow adoption is already happening. Employees are using AI tools outside formal systems. If leadership isn’t proactively shaping how AI is used across the business, it can lead to duplicated effort, inconsistent quality, and data governance risks. But it also reveals latent demand and the potential to scale what’s already working in pockets.
   
   Budget, time, and stakeholder alignment matter, too. An in-house build could take 12–24 months before delivering ROI. A vendor solution might deliver value in 6–8 weeks. The opportunity cost of delay, especially if competitors ship first, should weigh heavily in the decision.
4. Consider your data posture and compliance guardrails
   If the agent uses regulated or sensitive data such as health records, financials, or customer interactions, compliance becomes a gating factor. In some cases, the decision to build comes not from preference, but from necessity: the need to retain full control over data flows, storage, and inference behavior. In others, vendors offering private deployments or on-premise integrations may bridge the gap.
   The key question: can the agent operate within your governance frameworks without introducing risk?
5. Scan the vendor landscape honestly
   Not every tool labeled “AI agent” is truly autonomous. Some are just chatbots with scripts. If your use case is well-served by a mature, credible vendor with documented results, integration support, and active customers, that may be your fastest path to value. But if the market is flooded with hype and no solution meets your bar, building may be the only way to get the performance, compliance, and specificity you need.

Unlike traditional software, which runs fixed logic, AI agents interpret information dynamically. They can manage tasks end to end—triaging issues, integrating with tools, learning from results, and escalating when needed. Think of them less as apps and more as digital teammates.

These capabilities are already in play:

• JPMorgan’s COIN agent processes 12,000+ contracts annually and saves an estimated 360,000 hours of legal work per year.
• Bank of America’s Erica has handled over 2 billion interactions across 42 million customers.
• Procurement agents compare vendor quotes automatically. HR agents guide onboarding from start to finish. Sales agents log meetings, draft follow-ups, and recommend next steps based on pipeline data.

Not all agents are created equal

As powerful as these systems can be, not every AI-labeled tool qualifies as a true agent. Many offerings are just scripted workflows with a polished UI. Real agents exhibit autonomy, contextual reasoning, and the ability to operate across tools.

Key distinctions include:

• Autonomy: Can the agent operate without constant human input?
• Context memory: Does it understand what happened before, and adjust?
• System integration: Can it access tools, databases, and APIs to act?
• Adaptability: Does it improve with usage, feedback, or new data?

Considerations that influence build vs. buy

Understanding what agents can do is only half the equation. The build vs. buy decision depends on several deeper characteristics:

• Task complexity
  The harder the task, the smarter and more costly the agent needs to be. A simple routing bot is not the same as an autonomous risk assessment system.
• Data requirements
  Agents live and die by their data. Whether you build or buy, success hinges on access to high-quality, domain-specific inputs. No agent performs well on generic data alone.
• Ongoing tuning and maintenance
  Agents aren’t “set and forget.” They require continuous evaluation, retraining, and infrastructure support to stay useful and safe. This applies to both in-house and vendor-built solutions.
• Risk and reliability
  Even the most advanced agents can “hallucinate” or make errors. Human-in-the-loop controls, fallback paths, and performance monitoring aren’t optional—they’re foundational.
• Security and compliance
  Especially in regulated industries, data privacy and control can tip the scale toward building or demand specific vendor safeguards like private deployments.

For organizations with differentiated needs or strict control requirements, building in-house AI agents approach can offer real strategic value, but not without cost or risk.

• Control over data and IP: When you build, you own everything: models, logic, and infrastructure. That matters if your data is sensitive or regulated, or if the agent will handle proprietary workflows.
  JPMorgan, for example, built its COIN platform internally to maintain full control over legal data and system integration.
• Tailored to your business: In-house agents can be trained on your specific rules, edge cases, and systems. This is especially valuable for specialized processes that off-the-shelf tools can't handle. The closer the agent is to your core business logic, the more important customization becomes.
• Competitive differentiation: A well-built agent, tightly aligned to your domain, can become a defensible asset. When the capability itself is part of your value proposition, like underwriting logic, claims automation, or proprietary retrieval, it may be worth the investment to own it outright.
• Deeper system integration: Building allows you to fit the agent precisely into your stack, especially if you rely on legacy systems or tightly coupled data flows. Vendors may not offer this level of flexibility.
• Potential long-term efficiency: While building is expensive upfront, it can reduce recurring vendor costs. Over time, especially at scale, in-house agents may offer better total cost of ownership, assuming you can achieve and sustain performance.

• High resource and talent requirements: Advanced agents require top-tier AI engineers, data scientists, and MLOps talent; roles that are in short supply. Without the right team, in-house projects can stall or fail.
• Longer time to value: Building from scratch might take months, sometimes years of development before impact. If the use case is urgent, waiting 18–24 months could mean missed opportunities and slower outcomes compared to a vendor-led deployment.
• Ongoing maintenance load: Agents need constant tuning, retraining, compliance updates, and infrastructure support. Supporting a production-grade agent isn't a one-time project, it’s an ongoing operational commitment.
• Execution risk: Even after all the investment, your custom agent may not match vendor performance. Vendors often have larger R&D teams, more real-world data, and faster iteration loops. If their solution resolves 50% of cases and yours only hits 40%, the ROI gap can be significant.
• Hidden costs and tech debt: Integrating across systems, debugging data issues, and adapting to changing tech standards all add friction. If key engineers leave or your architecture isn’t future-proofed, the long-term burden can be steep.

The "buy" path involves adopting pre-built agent solutions from external vendors, whether through licensed software, API access, or platform subscriptions. The market for AI agents is growing rapidly and is projected to reach $47.1 billion by 2030. For many organizations, buying offers a practical, lower-friction route to deploying AI.

• Faster deployment and time-to-value: While a custom agent might take a year to build, an off-the-shelf product can often be configured and deployed in weeks. This accelerated timeline means faster ROI, especially critical for urgent use cases or early-stage experimentation.
• Lower upfront investment: Buying reduces the need for large-scale internal R&D, infrastructure, and specialized talent. Instead of building everything yourself, you pay a subscription or licensing fee for something already battle-tested. 
• Access to proven technology and expertise: Vendors constantly refine their products across deployments, integrating lessons from diverse use cases. As a buyer, you tap into that innovation. For example, a support agent trained on millions of tickets may outperform what an internal team could build on its own.
• Operational simplicity and ongoing support: With a vendor, most solutions come with support agreements, uptime guarantees, and product updates. Instead of owning maintenance and iteration, you can rely on a partner to carry that load, freeing your internal teams to focus on strategic priorities.
• Scalability and feature breadth: Many platforms offer elastic, cloud-based scalability and feature sets that go beyond what most internal builds can support. If your needs grow or evolve, buying often makes it easier to scale up or expand into new domains.

• Limited customization: Out-of-the-box agents are built for the average customer, not your exact workflows. You may need to compromise on functionality, adjust processes to fit the tool, or work around missing features. Some platforms support light customization or model extensions, but full flexibility is rare.
• Vendor lock-in and reduced control: Relying on a third-party vendor creates long-term dependencies. If the vendor changes pricing, discontinues a feature, or shifts roadmap priorities, you're constrained. Migrating off a commercial agent can be costly and disruptive, especially if integrations or data formats are proprietary.
• Security and compliance challenges: When using a vendor-hosted agent, your data often moves outside your controlled environment. Even with encryption and NDAs, this introduces risk, especially in regulated industries like healthcare or finance. Enterprises need to carefully vet data handling practices, retention policies, and compliance certifications.
• Integration gaps: Even the best commercial platforms may not integrate smoothly with your internal tools or legacy systems. APIs can help, but full end-to-end fit isn’t guaranteed. That means additional middleware work, or accepting partial automation.
• Ongoing costs and scaling limitations: While upfront costs are lower, long-term subscriptions can add up. If usage grows, so does your bill. In some cases, building may become more cost-effective at scale. But for many, the tradeoff in convenience and velocity makes recurring fees worthwhile.

The choice between building and buying AI agents isn’t always binary. Increasingly, enterprises are taking a hybrid path, combining in-house customization with third-party speed. This approach allows organizations to strike a practical balance: build what differentiates you, buy what doesn’t.

A typical hybrid model involves developing core, high-impact AI agents internally, especially when proprietary data or competitive differentiation is involved, while using vendor solutions for non-core functions. For instance, a financial firm might build a custom trading agent but license a prebuilt support chatbot.

Another common route: use no-code or low-code platforms to extend vendor solutions. This blurs the build-buy line. You’re buying a foundation, then building on top to meet specific needs. Similarly, organizations may start with open-source base models and fine-tune them internally, giving them more control without starting from zero.

What makes hybrid work:

• Modular architecture: Break complex workflows into parts. Build where it counts, buy the rest.
• API-first mindset: Ensure components connect cleanly—custom or not.
• Strategic partnerships: Co-develop features with vendors instead of going it alone.
• Phased rollout: Start with a vendor to learn fast, then bring key pieces in-house.

This model delivers speed, flexibility, and long-term control. As Nick Renotte, Chief AI Engineer at IBM Client Engineering notes, “AI is not a process in and of itself; it’s part of your daily work. We bake it into CRMs, into existing workflows, and quantify the value before scaling.”

No one-size-fits-all solution exists. But here’s a framework to guide your decision based on strategic alignment, internal readiness, and business constraints:

1. Strategic importance

• Build if the agent is core to your differentiation or IP.
• Buy if it supports general functions.

2. Customization needs

• Build for highly specific workflows.
• Buy if an 80% fit is good enough.

3. Internal talent and resources

• Build if you already have AI/ML teams.
• Buy if you’re resource-constrained.

4. Time-to-market

• Buy for quick wins or urgent needs.
• Build if the timeline is flexible and long-term control matters.

5. Data security and compliance

• Build for sensitive, regulated data.
• Buy only if the vendor meets all compliance standards.

6. Scale and long-term cost

• Build may save money at a high scale.
• Buy works better for moderate usage or uncertain growth.

7. Risk tolerance

• Build if your org can handle iteration and uncertainty.
• Buy if you need predictable results and vendor accountability.

8. Vendor landscape fit

• Buy if strong, proven vendors exist.
• Build if nothing fits well and customization is key.

Start small, then scale

You don’t have to decide everything up front. Many organizations start with a pilot, buying a vendor solution to test value in one workflow. From there, they either double down with the vendor or shift to an internal build. As Steve Moss, Director of watsonx® Americas at IBM says, "If you're waiting for perfect data to get started with agents, you're never going to move. Start small, refine fast, and scale as the data improves."

Finally, remember the landscape will change. What you buy today might be worth building in two years, or vice versa. Stay flexible, evaluate regularly, and align decisions to what gives your business the most leverage.

AI agents are already shaping the narrative of 2025. The most successful enterprises will be those that apply agents where they generate measurable impact, and govern them with clarity. As Gartner projects, by 2028, agentic AI will power 33% of enterprise software, resolving up to 80% of customer service queries and cutting costs by 30%.

Whether your goal is faster customer service, smarter sales support, or new product capabilities, the decision to build, buy, or blend AI agents should always reflect your strategic priorities.

At Turing, we help enterprises make these decisions with clarity. Our agentic systems are designed for composability, control, and results, built to integrate with your data, your tools, and your goals. Whether you need to co-develop a proprietary agent from scratch or embed proven capabilities into existing operations, we align to what drives value for your business.

Talk to a Turing strategist to define your next step.