Table of contents Toc Icon
Table of contents Toc Icon
Agentic AI is gradually becoming ubiquitous in the accounting industry as it has helped accountants introduce autonomy in the accounting process and transition into more advisory roles.
According to a Deloitte survey, 8 out of 10 accountants agreed that agentic AI would become a standard tool for them in the next five years. Gartner predicts that by 2028, at least 15% of day-to-day business decisions will be made autonomously through Agentic AI.
Agentic AI enables them to focus on specific goals rather than task completion. However, like any other technology, it requires the right infrastructure to work optimally, error-free, and uninterrupted.
Let’s try to find out the ideal infrastructure for deploying Agentic AI frameworks and tools for accounting to achieve maximum output.
Why Does IT Infrastructure Matter for Agentic AI
The Agentic AI setup requires different components for it to plan, respond, and iterate. For instance, you must set up a framework to manage and monitor the entire function. Moreover, you need different tools that agentic AI can utilize as per the goal.
For instance, AI agents can be connected with tools (CRM, ERP, and accounting software), APIs, and databases. Furthermore, a single request may require multiple AI agents.
The infrastructure requirements depend on how the agentic system is deployed. Firms using cloud-based APIs or SaaS tools may need limited local computing resources, while organizations hosting their own models may require substantial CPU, memory, storage, and, in some cases, GPU capacity.
Therefore, it necessitates that accounting firms plan, analyze, and research before adopting an IT infrastructure for Agentic AI.
Run AI workloads on scalable, secure cloud infrastructure built for performance.
Can Agentic AI Run on a Local Office PC?
Technically, the answer is yes. Agentic AI can run in a local IT environment. However, firms that have tried to do so face some limitations, such as –
1. High Cost of Deployment
As discussed in the previous section, AI agents require a high-performance infrastructure to function. Therefore, it demands significant capital investments from accounting firms for hardware procurement, deployment, and setup.
Moreover, as the framework evolves, you must be ready for regular hardware refresh cycles. In addition, firms have to bear recurring operational expenses of IT maintenance, replacements, monitoring, and workforce.
2. Limited Computing Resources
Even a moderate agentic AI framework requires multiple CPUs for tasks such as session initiation, calling, and execution. Moreover, you can require GPU support for AI inference. A local system normally used in office premises cannot fulfill the processing requirements of an agentic AI framework.
If the extensive AI workload is deployed on these desktops that already contain business applications, it can lead to system crashes and application shutdowns.
3. Data Security Considerations
Accounting firms handle sensitive client and internal data, such as financial reports, balance sheets, and payment information. With Agentic AI working on accounting workflows, it becomes even more vital to host the accounting process in a secure environment.
However, running Agentic AI locally is vulnerable to various risks, such as insider threats, ransomware attacks, phishing, malware, hardware malfunction, and accidental deletion. When the entire framework is hosted on a system, any minor threat can cause significant damage.
4. Limited Scalability
Agentic AI is a dynamic framework that adapts to changing requirements, goals, and strategies. Moreover, you may set up different frameworks for different goals. A local system setup has fixed specifications and is challenging to scale when demand rises. Consequently, during peak times, AI agents can utilize the entire IT resources, which can cause system crashes or latency.
5. No Continuous Availability
Some AI agents may need continuous access to applications, queues, databases, or event streams so they can monitor workflows and execute scheduled or event-driven tasks.
AI agents process invoices, reconcile accounts, review transactions, prepare reports, and organize client documents according to the goal set. However, a local workstation cannot run 24/7. When you deploy AI agents on a local system, every time the system shuts down, the AI process is halted or even disrupted.
Is Public Cloud the Best Option?
Public cloud provides computing services through infrastructure owned and operated by a third-party provider. Physical infrastructure may be shared across customers, although providers use logical isolation and may also offer dedicated compute options.
However, when it comes to data security or control, a local IT setup can be a better option. Here are some considerations before you choose public cloud for Agentic AI.
Shared Environment
Public cloud offers a multi-tenant solution, which means you are always sharing IT resources with others. Therefore, when multiple tenants experience peak periods and utilize maximum resources, it can hamper the performance of your AI workflow.
For instance, when AI agents access large databases or multiple tools simultaneously, they may require the full capacity of the cloud server. In that case, a shared approach can lead to performance issues.
Moreover, since accounting firms handle confidential information, it is unwise to host data in a shared environment. Although cloud providers ensure complete isolation between the tenants, you are still working on a shared server.
2. Limited Control
Unlike the local IT infrastructure, where you can make alterations, set up protocols, create policies, and deploy tools according to your requirements, with public cloud, these decisions are made entirely by the cloud service provider. Therefore, it leaves no scope for customization. If you want to create policies based on changing compliance rules or process demands, you are dependent on the provider.
Is Private Cloud The Best Approach?
Owing to its flexibility, security, and customizability, private cloud can be the best option for accounting firms. It offers the best of both worlds for firms, as you get optimum performance for AI agents and applications hosted in a secure environment. Let’s check out some parameters to consider before choosing a private server.
High-Performance Computing
Private cloud is built on a high-performance computing (HPC) infrastructure, which is ideal for parallel processing and complex computational tasks. You can opt for multiple vCPUs (virtual CPUs) that pool together their processing power to handle extensive AI workloads and large datasets.
AI agents can call multiple applications simultaneously without experiencing any performance dip. Moreover, unlike public cloud, you get dedicated resources for Agentic AI, ensuring stable performance, even during peak workloads.
GPU Support
In addition to CPU processing, AI agents require a GPU for graphics-intensive tasks like AI inference. A private server offers virtual GPU support, with the option to choose the GPU according to your requirements. Since the GPUs are virtual, you can easily upgrade or change them as your AI framework evolves.
24/7 Operations
In contrast to local systems, a private cloud server can support round-the-clock operations. It enables AI agents to continuously monitor, plan, respond, execute, and iterate without interruptions. Even if you switch off your remote device, operations still carry on at the backend servers.
Advanced Data Security
Private cloud offers a dedicated server for your AI agents. It adds a layer of security to the accounting process. It adds a layer of security to the accounting process. In addition, cloud service providers deploy advanced security features, including multi-factor authentication, data encryption (in transit and at rest), anti-malware, access controls, DDoS protection, IP filtering, OS patching, and more.
They have a team of cybersecurity experts that monitor the cloud infrastructure 24/7 for any anomalies and suspicious behavior.
Accommodates the Complete AI Ecosystem
Owing to the scalable cloud infrastructure, private cloud can enable accounting firms to host the entire agentic AI framework, all business applications, and large databases on a unified platform. It facilitates seamless integration between different systems, where you do not need to access data and tools on different servers. Consequently, it minimizes integration errors and enhances operational efficiency.
Local Workstation vs. Public Cloud vs. Private Cloud
| Local Workstation | Public Cloud | Private Cloud | |
| Performance | Dependent on the workstation specifications | Performance depends on service tier, instance type, architecture, and resource model | Potentially predictable performance with dedicated, properly sized resources |
| Scalability | Difficult to scale | Better scalability than local workstations | Scalable within provider capacity and contracted resources |
| Costs | High capital and operational costs | Lowest costs due to shared environment | Lower than local IT but higher than public cloud |
| Control | Total control over infrastructure | Extensive configuration options, but no physical infrastructure control | Considerable Control due to dedicated resources |
| Continuous Operations | Only when the PC is powered on and connected | Can facilitate 24/7 operations; it depends on the provider | Can support 24/7 operations with appropriate redundancy and SLA |
| Data Security | Vulnerable to ransomware, data theft, and hardware failures | Strong cloud security but shared infrastructure | Robust security due to advanced features and private resources |
| Maintenance | Requires regular maintenance from you | The cloud provider maintains the infrastructure | May be provider-managed, depending on the service agreement |
| Customization | Full control over customization | No control due to shared hosting | Better than public cloud due to private hosting |
Get dedicated cloud infrastructure for your accounting applications and AI workloads.
Agentic AI Is Here to Stay
Before deploying agentic AI across critical accounting workflows, firms should test the environment, define clear permissions and review processes, and confirm how data will be accessed, processed, stored, and protected.
The goal should not be to adopt the most powerful infrastructure available, but to build a reliable and secure foundation that enables AI to support accountants without compromising accuracy, oversight, or client trust.
Want to assess whether a managed private cloud fits your accounting applications and AI requirements? Book a free trial or chat with a cloud specialist.
