Agentic AI for Financial Services: Why ‘Buy vs. Build’ Is Dead and the Future Is ‘Buy‑and‑Build’
For years, large banks and financial institutions have been stuck on the same architectural question: Should we buy an AI platform or build our own? In 2026, that framing is not just outdated, it is actively slowing down serious AI adoption in financial services. The institutions that will win with agentic AI in financial services will buy and build: buy the right foundation, then build their proprietary edge on top.
When leaders search for ways to deploy agentic AI for financial services, they do not need another abstract “AI strategy.” They need a practical path that fits the realities of regulated environments, legacy estates, and tight risk controls. That is where buy‑and‑build comes in.
Why “buy vs. build” breaks down in financial services
Traditional “buy vs. build” assumes a clean, binary choice. In highly regulated, complex environments like financial services, that is a myth. The pace is set by regulation, policy, and control frameworks, not by how many engineers you hire.
Pure “buy”: A generic AI platform promises end‑to‑end transformation “out of the box.” In reality, it understands little about your products, your controls, your legacy estate, or your regulatory constraints. You get impressive demos, but struggle to get past narrow pilots and innovation‑lab experiments.
Pure “build”: A bank decides to build its own “agentic AI for banking” platform from scratch. After 18–36 months, you have a patchwork of services, partial integrations, and governance gaps, often without the resilience, tooling, or roadmap of a company whose sole job is to build such a platform. Meanwhile, the business has moved on.
Neither path is acceptable for mission‑critical workflows. You cannot outsource your edge, and you cannot afford to reinvent the underlying AI infrastructure alone, especially when your own change processes are intentionally conservative.
The buy‑and‑build model for agentic AI in banking
A more realistic pattern is emerging among forward‑leaning banks that want to operationalize agentic AI for financial services:
Buy a proven agentic AI layer
A secure, governed orchestration engine for AI agents.
Built‑in controls for data protection, model risk, and auditability.
Connectors and patterns that work with existing systems (core banking, trading, risk, ticketing, data platforms).
Build your proprietary workflows and policies on top
Encode your specific processes, risk policies, and exception paths.
Tune agent behavior to reflect your risk appetite and operating model.
Combine internal data, internal models, and third‑party services to create differentiated capabilities.
Crucially, buy-and-build respects the natural cadence of a bank. Your internal processes, governance forums, and committees move at the pace they need to. Artian moves faster behind the scenes, hardening the platform, adding capabilities, and integrating with your landscape, so that we are never the bottleneck when you are ready to take the next step. We meet banks where they are, then give them a way to accelerate safely.
This buy‑and‑build approach mirrors how banks already think about other critical infrastructure: you do not build your own database engine from scratch, but you certainly do not run your business on a generic schema either.
What big tech gets wrong about agentic AI in financial services
Large cloud and AI vendors are incredibly good at building horizontal technology. They have global footprints, mature DevOps practices, and powerful model stacks. They look like the “safe choice” for AI.
The challenge is that financial services is not a generic domain. A few recurring issues show up when banks rely solely on big‑tech offerings for agentic AI for banking:
Shallow domain understanding
Generic tools are not designed around the realities of KYC, AML, capital markets operations, wealth management, or collateral workflows.
“Use case templates” often ignore the messy edge cases, exception queues, and human governance structures that define real middle‑ and back‑office work.
Governance bolted on, not built in
Controls for explainability, approvals, and segmentation are usually layered on top of platforms originally designed for speed and scale, not for regulatory scrutiny.
It is difficult to align their generic governance models with your specific model‑risk, operational‑risk, and compliance frameworks.
Over‑promising, under‑delivering on integration
Slideware assumes clean APIs and modern microservices; reality is mainframes, message buses, Excel, and a decade of tactical integration.
You end up spending more time building glue code and workarounds than using the agentic capabilities you were sold.
This is not about bad technology. It is about fit. The same stack that works beautifully for e‑commerce recommendations or internal productivity bots is not sufficient, on its own, for governed, high‑stakes workflows in regulated financial institutions.
What organizations actually need from agentic AI platforms
CIOs, CTOs, COOs, and heads of operations in financial services tend to ask a different set of questions than generic “AI strategy” documents assume. Under the hood, they are really asking:
Can this platform live inside my control frameworks?
How does it handle model versioning, approvals, and monitoring?
Can I document and justify its behavior to regulators and internal audit?
Will this break my existing estate, or my SLAs?
Can agents interact with legacy systems via safe, well‑bounded integration patterns?
What happens to my SLAs and incident management if an agent misbehaves or a model degrades?
Can we phase in automation without losing control?
How do we start with human‑in‑the‑loop workflows and progress to higher autonomy where justified?
How do we ensure that exception paths are clear, observable, and explainable?
Does this help us make more money or spend less, measurably?
Which back‑ and middle‑office workflows are truly ready for agentic AI for financial services, and how do we prioritize them?
Can we get credible estimates of effort, impact, and risk before we commit to multi‑year programs?
Behind all of these is an implicit constraint: “We cannot and should not move faster than our risk and governance structures allow.” An effective platform for agentic AI in financial services must be designed around these questions and around that reality, not retrofitted to them.
How buy-and-build works with Artian
Artian was designed from the ground up for financial services, by people who have built and run systems in both global banks and large‑scale technology companies. That mix matters.
In a buy‑and‑build model, Artian provides the agentic foundation, and banks retain control of differentiation and cadence:
What you buy
A multi‑agent execution engine built for enterprise use: orchestration, coordination, and optimization across agents and services.
Governance features aligned with financial services expectations: data controls, approvals, audit trails, observability, and human‑in‑the‑loop patterns.
A library of workflow agents and skills targeting real banking use cases (e.g., reconciliations, incident/runbook management, pre‑trade and sales workflows, advisor assistance).
What you build
Your own workflows: how a claims queue should be triaged, how a runbook should be executed, how an exception should be escalated.
Your policies: risk thresholds, approval hierarchies, data‑access rules, model choices and combinations.
Your proprietary edge: internal models, internal data sources, and playbooks reflecting your specific products and culture.
How the pace works
You decide which workflows to target, how quickly to move them from assisted to automated, and which controls must be in place before each step.
We ensure the platform, integrations, and governance hooks are ready when you are, so the long pole in the tent is never “waiting for the vendor.”
This is how Artian “meets you where you are”: we respect the pace imposed by regulators, policies, and internal risk culture, while pushing our own roadmap, stability, and integration work forward as fast as possible behind the curtain.
The result is a structure where the heavy lifting of agentic infrastructure is shared, but the parts that define your institution remain yours. You stand on a specialized platform instead of starting from scratch, yet you are not locked into someone else’s idea of how your organization should operate.
Addressing the big concerns: risk, control, and lock‑in
It is natural for large financial institutions to be skeptical of any platform pitch. Three concerns surface repeatedly:
“We cannot outsource our risk and controls.”
Buy‑and‑build assumes exactly that: you do not outsource risk. The platform provides tools to encode and enforce your controls; it does not decide them for you.
Agentic workflows can be configured with explicit human checkpoints, approval steps, and escalation paths tuned to your risk appetite.
“We do not want another black box.”
Agentic AI for financial services must be observable: you need to see which agents did what, when, with which inputs, and why.
Traceability: every step, decision, and escalation logged and inspectable, is table stakes for regulated environments, not a premium feature.
“We already have cloud and AI vendors; why do we need another platform?”
Your existing vendors are critical for infrastructure and base models. Buy‑and‑build does not replace them; it sits on top, turning those raw capabilities into governed workflows tuned for financial services.
A domain‑specific agentic layer can orchestrate multiple models and services, including those from large vendors, under a single governance and observability framework.
How to start: pragmatic steps for CIOs and COOs
Moving from “buy vs. build” to “buy-and-build” does not require a big‑bang replatforming. In fact, it should not. A pragmatic approach looks like this:
Pick 1–2 high‑value, operational use cases
Focus on back‑ and middle‑office workflows with clear pain and measurable impact (e.g., reconciliation, incident/runbook management, complex exception handling).
Ensure they are important enough to matter, but not so critical that the risk profile becomes unmanageable as a first step.
Stand up an agentic foundation with guardrails
Integrate the platform with your identity, data‑access, and monitoring systems.
Define explicit human‑in‑the‑loop stages and success/failure criteria for the pilot workflows.
Co‑design workflows with operations and risk
Bring business, ops, tech and risk leads together in the design process from day one.
Map the current process, then jointly define where agents should act, where they should ask for help, and how their behavior will be evaluated.
Measure, iterate, then expand
Track not just speed or cost, but also error rates, exception volumes, and operator satisfaction.
Use successful patterns as templates for additional workflows, gradually expanding the surface area of agentic AI for financial services inside your institution.
Why domain expertise plus deep tech matters
At its core, buy-and-build is a recognition that no single party can do it all in modern financial services organizations:
Large tech companies excel at horizontal infrastructure and models, but are unlikely to live inside your regulatory and operational reality day‑to‑day.
Individual organizations understand their domain deeply, but rarely have the time or mandate to build and maintain a dedicated “best‑in‑class agentic AI platform” from the ground up.
Artian’s role is to sit at that intersection:
Deep technical experience from building and scaling systems at large tech firms and global banks, and financial institutions.
A laser focus on financial services, especially back‑ and middle‑office workflows that have been underserved by previous waves of automation.
A platform explicitly designed for buy‑and‑build: opinionated enough to be useful quickly, flexible enough to let you encode your own edge.
If you are still debating whether to buy or build your agentic AI capabilities, the more important question might be: What do you actually want to own?
For most institutions, the right answer is:
Own the logic, policies, and differentiation.
Partner on the agentic infrastructure that makes them safe, observable, and scalable. Financial services and banks will always move at the pace their controls demand. Artian’s job is to move faster on our side, so when you are ready to take the next step with agentic AI, the platform is already there waiting, not the other way around.
That is the future of agentic AI, and it is very much a buy-and-build world.
