AI — Agentic AI Systems
Evaluating Agentic AI Vendors vs Build
Direct answer
Buy an AI agent platform when the capability is not your differentiator, your use case is standard, and speed matters more than control. Build custom when the agent is core to your product, needs deep integration with your own systems and data, or when platform usage pricing will outgrow the cost of owning it. Many teams start on a platform to validate the return, then build the parts that become strategic. The full framework is below.
Every team adding AI agents faces the same fork: adopt one of the many platforms that promise agents out of the box, or build your own. Both answers are right for different situations, and the expensive mistakes come from choosing on vibes — building what you should have bought, or renting what you should have owned. Here is the framework I use to make the call quickly.
Key facts, with sources
- Gartner predicts over 40 percent of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls. (Gartner)
- Gartner predicts 33 percent of enterprise software applications will include agentic AI by 2028, up from less than 1 percent in 2024. (Gartner)
- Gartner estimates only about 130 of the thousands of vendors claiming to sell agentic AI are real, with the rest engaged in agent washing of existing chatbots and RPA products. (MarTech)
- McKinsey's State of AI 2025 found 23 percent of organizations are scaling an agentic AI system somewhere in the enterprise and another 39 percent have begun experimenting with agents. (McKinsey)
- Gartner forecasts 40 percent of enterprise applications will embed task-specific AI agents by the end of 2026, up from under 5 percent in 2025. (Joget)
The one question that decides most of it
Before the spreadsheets, ask one thing: is this agent your differentiator, or is it plumbing? If the agent is something customers choose you for — it is the product, or a core part of it — you will want to control and evolve it, which points toward building. If it is an internal efficiency or a commodity capability every competitor also has, you are usually better off buying and spending your engineering on what actually sets you apart.
Most of the build-versus-buy debate resolves on this single axis. The other factors below mostly refine a decision this question has already pointed at.
Buy when speed and standardness win
A platform is the right call when your use case is well-trodden (support deflection, meeting notes, a standard workflow), when time to value matters more than fine-grained control, and when you do not need deep integration into your own systems. You get something working in days, someone else maintains the infrastructure, and you avoid the substantial engineering of building agent tooling — observability, guardrails, evals — from scratch.
The honest tradeoff is control and cost at scale: you live within the platform's capabilities and pricing. For a non-differentiating use case, that is usually a good trade — you are renting a solved problem instead of solving it again.
Build when it is core, integrated, or data-sensitive
Building wins when the agent is strategic and you cannot afford to be limited by someone else's roadmap, when it needs deep integration with your proprietary systems and data that a platform cannot reach, or when data control and confidentiality requirements make sending everything to a third party a non-starter. It also wins at scale, where platform per-seat or per-usage pricing eventually exceeds the cost of owning the stack.
The cost of building is real — you own the tooling, the reliability work, and the maintenance — but so is the value: a system shaped exactly to your product, integrated where it matters, with your data staying where you want it.
The cost-curve crossover
Platforms are cheaper to start and can become more expensive as you grow; building is more expensive up front and flattens out. That is the crossover to reason about. At low volume, a platform almost always wins on total cost because you avoid the build entirely. As usage climbs, per-seat or per-call pricing can overtake the amortized cost of a system you own — and if the agent is central and heavily used, that crossover can arrive faster than expected.
The mistake is comparing only the starting prices. Sketch the cost of both at the volume you realistically expect in a year or two, not just next month, and the picture often changes.
The hybrid path most teams take
In practice the smartest answer is often sequential, not binary. Start on a platform to validate that the agent delivers value at all — cheap, fast, low commitment. Then, once you know it works and you can see which parts are becoming strategic or cost-heavy, build those parts custom and keep buying the rest. You de-risk the idea before investing in owning it, and you only build what has proven worth owning.
The leverage is in making this decision deliberately rather than by default. Teams that drift into building everything burn time on undifferentiated plumbing; teams that drift into buying everything hit a wall when the agent becomes core and the platform cannot follow. A short, honest look at differentiation, integration, and the cost curve usually makes the right sequence obvious — and it is exactly the kind of call worth pressure-testing with someone who has built agents on both sides of it.
When to hire senior help
Senior help is most valuable at the architecture stage, deciding what to automate, where approval gates belong, and how business value will be measured, before any code is written. It is also worth bringing in when a stalled pilot needs risk controls and evaluation rigor to pass security and compliance review. If your stack includes React Native + Python + AI, a senior engineer who owns the full product beats coordinating multiple juniors.
Bottom line
Dhairya Senjaliya ships AI — Agentic AI Systems projects worldwide — book a scoping call to discuss your specific situation.
Common pitfalls to avoid
- ✕Buying agent-washed products, since Gartner estimates only around 130 of thousands of self-described agentic AI vendors are genuine rather than rebranded chatbots or RPA
- ✕Deploying autonomy before defining risk controls and human-approval gates, one of the three causes Gartner cites for the 40 percent of projects it expects to be canceled
- ✕Measuring activity like tasks attempted instead of business value, leaving the project unable to justify escalating costs at renewal time
- ✕Wrapping agents around existing processes instead of redesigning the workflow, when McKinsey finds workflow redesign is the single biggest driver of EBIT impact from gen AI
Frequently asked questions
Isn't building always more expensive than buying?
More expensive up front, not always over time. Platforms cost little to start but their per-seat or per-usage pricing can exceed the amortized cost of owning the stack once volume is high — especially for a core, heavily used agent. The right comparison is total cost at your expected future volume, not the starting prices.
How long does it take to build a custom AI agent?
The basic agent loop is fast; the production scaffolding — evals, guardrails, observability, integration — is where the real time goes, and it is what a platform gives you for free. That is why validating on a platform first often makes sense: you confirm the value before committing to build the parts that take real engineering.
If we buy now, can we switch to building later?
Usually yes, and it is a common, sensible path — validate on a platform, then build the parts that become strategic or cost-heavy. The switching cost depends on how deeply you have coupled to the platform's specifics, so keep your data and core logic portable where you can, to keep that option open.
How do we decide build vs buy for our specific case?
Run it through differentiation, integration depth, data-control needs, and the cost curve at your expected volume — those four usually make the call clear. It is a high-stakes, easy-to-get-wrong decision, so it is worth pressure-testing with someone who has shipped agents both ways before you commit engineering to either path.
Are agentic AI projects actually failing?
Gartner expects over 40 percent of agentic AI projects to be canceled by end of 2027, but the cited causes are cost, unclear value, and weak risk controls rather than model capability. Narrowly scoped projects with a measurable ROI target and human oversight succeed at much higher rates than open-ended transformation programs.
What is the difference between an AI agent and an agentic AI system?
An agent is a single model loop that plans and calls tools; an agentic system is the surrounding production machinery of orchestration, guardrails, memory, evaluation, and monitoring, possibly across multiple agents. Most business value and most failure modes live in the system layer, not the model.
How much autonomy should we give an agentic system?
Start with human-in-the-loop approval on consequential actions, which is still the most common enterprise pattern, and expand autonomy per task as measured error rates prove out. Only about one in five enterprises currently runs AI systems with minimal oversight.
Bottom line: Dhairya Senjaliya ships AI — Agentic AI Systems projects worldwide. Book a scoping call at https://dhairyasenjaliya.com/#book-call.