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Build vs Buy AI Agents: How enterprises are making the decision

The build vs buy decision for AI agents is different than for traditional software. Lambda, a $4B+ AI company with world-class engineers, chose to buy. Here's what enterprises are learning.

Last updated: February 2026

Quick summary

The build vs buy question for AI agents is not the same as for traditional software.

With most enterprise software, building internally means slower delivery but more control. With AI agents, the calculus shifts. The field evolves fast. The talent required is specialized and scarce. The opportunity cost of pulling engineers off core product work is higher than most teams estimate. And the gap between a working prototype and a production-grade enterprise deployment is wider than it appears.

This page walks through both sides of the decision honestly. There are situations where building makes sense. There are situations where buying makes sense. The goal is not to convince you of one answer. It is to help you think through the decision with the right information.

One signal worth noting up front: Lambda, a $4B+ AI infrastructure company with approximately 500 employees and some of the strongest AI engineers in the industry, evaluated both options seriously. They chose to buy. Their reasoning is worth understanding, even if your situation is different.

The landscape: why this decision is harder than it looks

The enterprise AI agent market is moving fast. Gartner predicts that 40% of enterprise applications will feature task-specific AI agents by 2026, up from less than 5% in 2025. That pace of change is part of what makes the build vs buy decision so consequential.

A few data points that frame the challenge:

  • Most internal AI projects stall. Only 11% of organizations have AI agents in production. The rest are stuck in pilot programs, abandoned after cost overruns, or quietly shelved.
  • Budget estimates are consistently low. Research shows most enterprise teams significantly underestimate the true total cost of ownership for AI agent development. Data preparation alone accounts for up to 80% of total project effort.
  • The failure rate is high. Forrester predicts that 75% of companies attempting to build their own agentic systems will fail, citing the complexity of requiring "diverse and multiple models, sophisticated retrieval-augmented generation stacks, advanced data architectures, and niche expertise."
  • Infrastructure keeps shifting. New frameworks, APIs, and orchestration layers emerge faster than organizations can standardize or validate them. Popular tools like LangChain, LangGraph, AutoGen, and CrewAI are evolving rapidly. What you build today may need to be rebuilt in six months.

None of this means building is always wrong. It means the decision deserves more rigorous analysis than most teams give it.

Why enterprises consider building

The instinct to build internally is reasonable. It usually comes from three places:

Control. When AI agents are handling high-volume enterprise workflows (customer onboarding, sales research, compliance monitoring) the organization wants full control over how the agent behaves, what data it accesses, and how it makes decisions. Building internally feels like the path to maximum control.

Customization. Every enterprise has unique workflows, systems, and edge cases. The concern is that an external platform will not accommodate the specific logic, integrations, and exceptions that make your business different. Building internally means the solution is tailored exactly to your environment.

Existing engineering talent. If you already have strong engineering teams, the logic seems straightforward: why pay someone else to build what your own people can build? The team knows your systems, your data, your requirements. They should be able to deliver something better than a vendor could.

These are legitimate reasons. For some companies, they are the right reasons.

But for many enterprises, the reality of building turns out to be harder than the initial assessment suggests. And the definition of "buy" has changed in ways that address many of these concerns directly.

Why building is harder than expected

The enterprises we work with that considered building, and some that started building, tend to run into the same set of problems.

The timeline problem

AI agent development takes longer than most teams estimate. The initial prototype comes together in weeks. But moving from prototype to production (handling edge cases, building governance, ensuring reliability across thousands of interactions, integrating with multiple enterprise systems) stretches to 6-12 months. Sometimes longer.

During that time, the business problem the agent was supposed to solve is still being handled manually. The ROI clock does not start until the agent is in production. Every month of development is a month of value left on the table.

Gartner projects that over 40% of agentic AI projects will be canceled by 2027, not because the models fail, but because organizations struggle to operationalize them. The gap between "demo" and "production" is where most internal builds stall.

The opportunity cost problem

This is the one that surprises most teams.

Engineering is not sitting idle. They are working on core product, infrastructure, customer-facing features, and a backlog of other priorities. Every engineer assigned to build an AI agent is an engineer pulled off something else.

For product companies, this trade-off is especially painful. The agent might save operations time, but it costs product development time. And unlike the operations savings, the product development cost does not appear on a spreadsheet. It shows up months later as features that did not ship, customers that were not served, competitive ground that was lost.

McKinsey estimates that as organizations bring more technology management in-house with agentic AI, the core business of technology services providers could face a 20 to 30 percent contraction. The engineering hours are real, and they come from somewhere.

The maintenance problem

Building the agent is the beginning, not the end. AI agents require ongoing maintenance that traditional software does not:

  • Model updates and prompt engineering as AI capabilities evolve
  • Integration maintenance as connected systems change their APIs
  • Performance monitoring and quality assurance across thousands of interactions
  • Governance and audit trail management
  • Adaptation to new edge cases as the business evolves

Enterprise AI agent development costs $75,000-$500,000+ for the initial build, with annual maintenance running 15-25% of that initial investment. Companies that have built automation tools before AI agents know this pattern: the maintenance burden often exceeds the original build effort within the first year.

The AI specialization problem

Most enterprise engineering teams are strong software engineers. That does not automatically make them strong AI engineers. Building reliable, production-grade AI agents requires specific expertise: prompt engineering, model evaluation, guardrail design, hallucination management, agent orchestration, and decision traceability.

A survey of over 1,000 enterprise technology leaders and practitioners found that 86% of enterprises need upgrades to their existing tech stack to deploy AI agents. Security concerns are the top challenge, cited by 53% of leadership and 62% of practitioners. Separately, Lyzr AI research found that 62% of enterprises exploring AI agents lack a clear starting point.

These skills are different from building a web application or maintaining an ERP integration. Teams learn them, but the learning curve adds months to the timeline, and the mistakes made along the way can be expensive in a production environment.

The stack instability problem

This is a challenge unique to AI agent development compared to traditional software. The AI infrastructure layer is evolving rapidly. New model releases, new orchestration frameworks, new API patterns, new security requirements. What your team builds on today may need to be rearchitected in months as the ecosystem shifts.

Internal teams must absorb that instability. They need to track model changes, evaluate new frameworks (like Haystack and AutoGPT), manage version migrations, and make architectural decisions in a landscape that has not stabilized. A platform that serves hundreds of enterprise deployments can absorb that complexity and pass the stability to you. An internal team of three or four engineers cannot.

What "buy" actually means (it is not what most teams expect)

When enterprises think "buy," they often imagine a SaaS black box: a rigid product where you are limited to the features the vendor decided to build. Pick from a menu. Configure a few settings. Hope it fits.

That is not what buying AI agents looks like when done right. And the distinction matters, because the best version of "buy" addresses every concern that drives teams to build.

Platform plus service, not just software

The most important shift in understanding the buy option: the right partner provides a platform AND a dedicated service layer. Not software you configure alone. Not documentation links. A combination of technology and embedded engineering support designed for enterprise-grade deployment.

What this looks like in practice:

Platform: your team builds agents specific to your workflows. You define the logic, the integrations, the escalation rules, the guardrails. The platform handles the AI infrastructure, the reliability, the governance. 4,000+ pre-built integrations connect to your actual tech stack (CRMs, ERPs, Slack, Teams, WhatsApp, email) so agents operate where work already happens. No new systems. No infrastructure rebuild.

Forward Deployed Engineers embedded with your team. This is the part most "buy" options miss. Real engineers, embedded in your organization, who help you identify the highest-impact use cases, design agents that fit your specific reality, handle integration complexity, and run pilots without requiring your internal resources. They are not support tickets. They are engineers who understand your environment.

Change management, not just deployment. Deploying AI at scale is 10% technology and 90% organizational change. Forward Deployed Engineers help frame the change for your teams, train people on new workflows (hands-on, not just documentation), build confidence through small wins before scaling, and address concerns about transparency and control.

Ongoing optimization. Agents improve with use. Your FDE team helps analyze what the agent is doing well, identify patterns where the agent should escalate, refine the agent's logic based on real-world feedback, and scale agents to new teams and processes.

Business teams own it

The people closest to the workflow build and manage the agent. No engineering dependency. No IT backlog. The sales ops leader builds the sales research agent. The support team lead builds the support triage agent. Ownership stays with the people who understand the work.

This is not about removing technical depth. It is about removing the bottleneck. When the business team needs to adjust the agent's logic, they do it in hours, not sprint cycles. When a new edge case emerges, they handle it directly instead of filing a ticket and waiting.

Enterprise-grade from day one

SOC 2 Type II, ISO 27001, ISO 42001, GDPR. Full audit trails, decision traceability, role-based access. These compliance requirements take months to build internally. With a platform that has them built in, they are available on day one.

The result

Something that looks more like a partnership than a software purchase. Your team builds what is specific to your business. The platform handles what is common to every enterprise AI deployment. Forward Deployed Engineers bridge the gap between the two.

This is why the traditional "build vs buy" framing can be misleading. The real comparison is: build everything yourself, or work with a team that provides the technology, the engineering support, and the change management to deploy faster, with less risk, and without pulling your engineers off core product work.

The Lambda story: a $4B+ AI company that chose to buy

This is the proof point that tends to shift the conversation for enterprises evaluating the build vs buy decision. Because if any company had the talent and resources to build AI agents internally, it was Lambda.

Lambda is a $4B+ AI infrastructure company. They build supercomputers for AI training and inference. Their team of approximately 500 employees includes machine learning engineers published at NeurIPS and ICCV. AI is literally their business.

They chose to buy.

What Lambda needed

Lambda's sales intelligence team needed to monitor 12,000+ enterprise accounts, tracking funding announcements, leadership changes, infrastructure investments, technical hiring patterns, product launches, and competitive movements. Done manually, this required thousands of analyst hours every month.

As Joaquin Paz, Lambda's Head of Sales Intelligence, described it: "We were making trade-offs we didn't want to make. We could either focus on our top 50 accounts and ignore the rest, or spread thin across thousands and miss critical opportunities. Neither option was acceptable."

What Lambda tried first

Before choosing a platform, Lambda explored two paths:

Open-ended AI agents. Tools like ChatGPT Deep Search. Powerful, but inconsistent. Ask the same question twice, get different results. For enterprise sales intelligence, where reliability matters, this unpredictability was unacceptable.

Traditional automation. Workflow automation platforms. Consistent, but rigid. Heavy hard-coding, extensive upfront configuration, brittle integrations. They could not reason about what mattered or adapt when priorities shifted.

Lambda needed something that combined intelligence with reliability, without the downsides of either.

Why Lambda did not build internally

This is the critical part. Lambda had the engineers. They had the AI expertise. They had the infrastructure. Their leadership seriously considered building.

The conclusion: the opportunity cost of engineering time was too high. Every engineer assigned to build internal sales intelligence agents was an engineer not working on Lambda's core product, AI infrastructure. For a company competing at the frontier of AI compute, that trade-off was unacceptable.

And it was not just about the initial build. It was about the ongoing maintenance, the model updates, the integration management, and the iteration cycles. All of that engineering attention, indefinitely, pulled away from the work that drives Lambda's revenue and competitive position.

What happened instead

Joaquin Paz, Lambda's Head of Sales Intelligence, built the agent himself. He has no engineering background.

"I'm not an engineer. I built this in days. With the automation tools we looked at before, I would have needed to spec everything out and wait months for development."

The agent went into production within weeks, not months. It now monitors 12,000+ enterprise accounts annually, performing the kind of analysis that would require 2 hours of manual work per account.

The results

  • $4B+ in cumulative pipeline identified across accounts Lambda was not actively monitoring
  • 24,000+ research hours added annually (equivalent to 12 full-time analysts)
  • 12,000+ enterprise accounts analyzed with deep competitive intelligence each year
  • $7M+ projected annual value as Lambda expands from a single agent to an agent fleet

From one agent to an agent fleet

Lambda has since expanded beyond a single agent. They are building what they call an "agentic layer": a network of specialized agents across sales intelligence, marketing operations, and customer engagement. Each new agent deploys in days and builds on the foundation they have already established.

"We're not building separate automations. We're building an intelligent layer that understands how Lambda works. Each agent we add makes the foundation stronger."

-- Joaquin Paz

The key insight: Lambda did not just save engineering time on one project. They established a model where business teams can deploy new agents independently, without competing for engineering resources. Each subsequent agent is faster than the last because the infrastructure, integrations, and governance are already in place.

What Lambda's choice signals

The takeaway is not that every company should make the same choice Lambda made. The takeaway is this: a company with $4B+ in valuation and world-class AI engineers looked at the build option seriously, evaluated the full cost (not just the initial build, but the ongoing maintenance, the opportunity cost, and the time to value), and decided that buying was the stronger path.

If Lambda, with AI as their core competency, concluded that the opportunity cost was too high, most enterprise engineering teams should ask themselves the same question honestly.

When building makes sense

Being honest about this: there are situations where building internally is the right call.

AI agents are your product. If you are an AI company and the agents you are building are what you sell to customers, building makes sense. That is core IP, and it should stay in-house. Lambda builds AI infrastructure (their product). They bought for internal operations (not their product). The distinction matters.

You have deep AI R&D resources with bandwidth. Not just strong engineers, but engineers with specific AI agent experience who are not needed on other priorities. This is rare, but it exists. If your team has already shipped production AI agents and has the capacity to build more, the learning curve argument does not apply to you.

The use case is so specialized that no platform can accommodate it. Some workflows involve proprietary algorithms, unique data structures, or domain-specific logic that genuinely cannot be served by a platform. This is less common than teams think, but it is real in certain cases.

You have unlimited engineering runway and no time pressure. If the timeline does not matter and engineering capacity is not a constraint, building internally will eventually produce a more customized solution. The question is whether "eventually" is soon enough.

A simple honesty test: Before committing to build, ask your engineering leadership two questions. First: "If we assign engineers to this, what will they not be working on?" Second: "How many production AI agents has this team shipped before?" The answers usually clarify the decision.

Most enterprises we talk to do not match these criteria. Their engineering teams are stretched. Their timelines are tight. The AI agents they need are operational, not product, meaning the build vs buy math favors speed and time-to-value over maximum customization.

When buying makes sense

Speed to value matters. If the business problem is costing you money or leaving revenue on the table every month, the difference between "deployed in weeks" and "deployed in 6-12 months" is significant. Lambda's Head of Sales Intelligence built his agent in days. An internal build at the same company would have taken months.

Business teams need ownership, not engineering dependency. The people who understand the workflow should be the ones building and managing the agent. If the sales ops team has to wait for engineering to build, iterate, and maintain their sales research agent, the feedback loop is too slow. A platform that lets business teams build and own their agents eliminates this bottleneck.

Opportunity cost is real for your engineering team. If your engineers are already fully allocated to core product work, pulling them off to build operational AI agents has a direct cost. Lambda, with some of the strongest AI engineers in the industry, concluded this cost was too high. For most enterprise engineering teams, who are less AI-specialized than Lambda, the cost is even higher.

You need enterprise-grade governance on day one. SOC 2, ISO 27001, audit trails, decision traceability, GDPR compliance. Building these from scratch takes months. If your compliance requirements are non-negotiable (and in enterprises, they always are), a platform that ships with these capabilities saves significant time and risk.

You want to start with one agent and scale to many. The first agent proves the model. The second and third build on the foundation. A platform approach means each subsequent agent deploys faster than the last, because the infrastructure, integrations, and governance are already in place. Lambda started with one sales intelligence agent. They are now building an entire agent fleet across sales and marketing.

You want dedicated engineering support, not just software. If your team does not have AI agent expertise in-house, the Forward Deployed Engineer model means you get experienced engineers embedded with your team from day one. They bring pattern recognition from dozens of enterprise deployments. That expertise accelerates your timeline and reduces the risk of costly mistakes that first-time AI teams commonly make.

The hidden costs of building

When teams estimate the cost of building AI agents internally, they tend to account for development time and underestimate everything else.

Hiring or reallocating AI specialists

If your current engineers are not AI specialists, you either need to hire or retrain. AI engineering talent is expensive and competitive. And once hired, they need to stay, because the agent requires ongoing expertise, not a one-time build. At current market rates, a small team of AI engineers (2-3 people) costs $600K-$1M+ annually in fully loaded compensation before they have built anything.

Iteration speed

AI agents do not ship once and work forever. They need constant iteration: refining prompts, adjusting guardrails, expanding capabilities, improving edge case handling. When the business team that owns the workflow has to file engineering tickets to make changes, the iteration cycle slows from hours to weeks.

The companies we work with see a different pattern: the business team that owns the workflow iterates on the agent directly. Changes deploy in hours, not sprint cycles. This speed difference compounds over months.

Maintenance burden

Every integration, every connected system, every API changes over time. When they change, the agent needs to be updated. With internal builds, this maintenance falls on the engineering team that built it, adding to their backlog indefinitely. With a platform approach, integration maintenance is handled by the platform team, not yours.

Governance and compliance

Building audit trails, decision traceability, role-based access, and compliance reporting from scratch is a project in itself. And it is not optional for enterprise deployments. This work is invisible in initial build estimates but adds months to the actual timeline. One CTO we spoke with described compliance as "the second project hiding inside the first one."

The compound cost

Add it up: hiring, iteration speed, maintenance, governance, opportunity cost. The initial build estimate, the one that got budget approved, rarely captures the full picture. Research confirms that the majority of enterprises significantly underestimate total cost of ownership for AI projects. The real cost becomes clear 12-18 months in, when the agent requires more ongoing engineering time than anyone planned for.

A framework for the decision

If you are in the middle of this decision, here is a structured way to think through it.

Question Favors build Favors buy
Are AI agents your core product? Yes, agents are what you sell No, agents support internal operations
Does your team have production AI agent experience? Yes, they have shipped agents before No, this would be their first
Is your engineering team at capacity? No, they have available bandwidth Yes, they are fully allocated to core work
How urgent is the business need? Timeline is flexible (6-12+ months is fine) Every month of delay has a measurable cost
Do you need enterprise governance on day one? You have governance infrastructure already You would need to build it from scratch
How many agents will you need over the next 12 months? Just one, highly specialized Multiple, across different departments
What is the opportunity cost of engineering time? Low (engineers are not needed elsewhere) High (every hour has a competing priority)

If most of your answers fall in the "favors buy" column, the pattern is clear. If most fall in "favors build," building may genuinely be the right path for your organization.

Frequently asked questions

What if we already started building?

Sunk cost should not drive the decision. The question is: what is the fastest path to production value from where you are now? Some companies we work with started building internally, realized the timeline was longer than expected, and deployed with a platform while repurposing the internal work. Others completed their build and use a platform for subsequent agents. The two approaches are not mutually exclusive.

Can we build some agents and buy some?

Yes, and many enterprises do. The pattern that tends to work: build internally when the agent is deeply tied to core product IP. Buy for operational agents (sales, support, marketing, HR) where speed and business team ownership matter more than maximum customization. Lambda, for example, has deep AI engineering for their core product but chose to buy for their go-to-market agents.

What is the total cost comparison?

It depends on the use case, but the comparison should include: engineering salaries (fully loaded), opportunity cost of engineers not working on core product, maintenance costs (ongoing, not just build), hiring costs for AI specialists if needed, and the timeline cost, meaning the value lost while the agent is being built instead of operating. Lambda's agent surfaces $4B+ in pipeline visibility. Every month of development delay is a month of that value unrealized.

How customizable is a platform solution?

More than most teams expect. With a platform approach, your team defines the workflow logic, the integrations, the escalation rules, the guardrails, and the business rules. The platform handles the AI infrastructure, reliability, and governance. Lambda's agent is fully customized to their sales intelligence workflow: monitoring specific signals, analyzing specific account types, delivering insights in their specific format. It was built by their sales intelligence team, not by external engineers.

What about vendor lock-in?

A fair concern. The right question is: what does the platform actually own? If the platform owns your workflow logic and data, lock-in is real. If the platform provides infrastructure and your team owns the agents, the logic, and the data, the risk is lower. The deeper question is whether the speed-to-value and ongoing platform benefits outweigh the switching cost. For Lambda, the answer was clear: the opportunity cost of not using a platform was far higher than the theoretical cost of switching later.

What does "Forward Deployed Engineers" actually mean?

Forward Deployed Engineers (FDEs) are real engineers embedded with your team. They are not a help desk. They are not a support chat. They work alongside your people to identify the highest-impact use cases, design agents for your specific workflows, handle integration complexity, and ensure the deployment succeeds. Think of them as an extension of your team that brings AI agent expertise you do not have to hire for permanently. This model exists because deploying AI at scale is as much about organizational change as it is about technology.

How is this different from hiring an agency to build for us?

This is the same structural problem that plagues AI consulting and outsourcing firms. Agencies build a solution and leave. You are left with something you do not fully understand and cannot easily modify. Every change requires going back to the agency, waiting for availability, and paying for more hours. With the platform plus FDE model, your team builds and owns the agents. The FDEs help you develop internal capability, not external dependency. When the FDE engagement evolves, your team has the knowledge and the tools to continue independently.

Worth exploring?

If your team is evaluating the build vs buy decision for AI agents, it might be worth understanding how Lambda, a $4B+ AI company with world-class engineers, made their choice. Or how Orange, a multi-billion euro telecom with 120,000+ employees, had their business team deploy agents in weeks that delivered $4M+ in yearly revenue.

Every engagement starts with a 3-month proof of concept tied to specific business outcomes. Forward Deployed Engineers are embedded with your team from day one. You can exit anytime.

[Read the full Lambda case study -->]

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