Euphoria around generative AI in the enterprise sector is giving way to rigorous engineering pragmatism. Businesses have realized that the effectiveness of AI agents is determined not so much by the power of large language models (LLMs) as by infrastructure maturity and corporate data quality. According to the Cisco AI Readiness Index 2025, only 13% of organizations are classified as "Pacesetters"—leaders that consistently derive real value from AI implementation. Their main differentiator is a systematic focus on data readiness. Most companies, after initial pilots, encounter "AI bloat"—chaotic infrastructure expansion where uncontrolled model requests cause unpredictable spikes in cloud bills.
Anatomy of AI bloat: why chaotic LLM requests drain cloud budgets
The problem of AI bloat intensifies when system architecture is built on a "model-first" principle. Instead of pre-structuring the information space, developers rely on the capabilities of the models themselves, attempting to compensate for data chaos with massive context windows and complex reasoning cycles (chain-of-thought). This leads to three classic infrastructure traps:
- Duplication of data pipelines: several AI agents independently import and process the same raw data, doubling computing and storage costs.
- Lack of detailed LLM API cost allocation: without granular transaction-level tags, it is impossible to determine which business unit or process triggered a sharp increase in the cloud provider's bill.
- Inconsistent data quality across departments: dirty and fragmented data force AI agents to spend an excessive number of tokens on repeated prompts, context clarification, and correcting their own hallucinations, which critically degrades latency and system economics.
Data readiness as an infrastructure foundation: lessons from market leaders
Data readiness is not a one-time database cleanup initiative but a fundamental engineering discipline. Attempting to integrate an agent with an unstructured mass of corporate documents (e.g., through "naive" RAG on raw PDF files) leads to exponential token consumption growth. The NIST AI Agent Standards Initiative emphasizes the critical need for standardized, architecturally secure approaches to developing systems with AI agents.
A data-centric architecture requires that the model does not parse unstructured junk. Instead, the infrastructure must provide the agent with pre-structured, validated, and filtered context through clear API contracts.
Data-centric architecture in practice: structuring data with UnityBase
Implementing a data-centric approach requires a reliable architectural layer that unites data models, APIs, and strict access rules. One such solution is the full-stack JavaScript low-code platform UnityBase, a joint development of the Intecracy Group technology alliance (where InBase is a key, but not the only, developer).
UnityBase uses a unified domain model (Domain metadata) to describe and structure corporate data, automatically generating secure REST APIs based on it. Thanks to built-in row-level security (RLS) and access control lists (ACL) in commercial editions, the platform ensures that an AI agent receives only the information that matches its current permissions and request context.
Custom development of AI systems using such infrastructure solutions is provided by Softengi, which holds certification for the international AI management standard ISO/IEC 42001:2023. Using UnityBase as a foundation allows Softengi to build solutions where data is cleaned and validated before it enters a prompt. This narrows the context to a relevant minimum, reduces the risk of hallucinations, and ensures the predictability of cloud costs.
FinOps for AI agents: moving from total bill monitoring to unit economics
According to the FinOps Foundation methodology, mature infrastructure management requires a transition from passive total bill monitoring to unit economics assessment. For AI agents, this means calculating the cost of processing a single business transaction.
Implementing FinOps practices at the architectural level includes:
- Management via API Gateway: proxying all model calls for centralized token count logging and rate limiting.
- Granular tagging: dynamic labeling of each request with user, department, and specific process metadata for transparent cost allocation.
- Routing optimization: using smaller, specialized models for intermediate classification tasks, leaving heavy LLMs only for final synthesis tasks.
Designing without surprises: cost optimization at the system design stage
As noted in Microsoft's recommendations (Azure Well-Architected — Cost Optimization), modeling costs at the cloud infrastructure design stage is a key tool for avoiding overspending. Post-factum optimization, once the system is already running in production, is the most painful and expensive path.
Architects must pre-configure mechanisms for semantic caching, limiting the depth of recursive agent queries, and setting strict token limits at the business role level. Only a combination of prepared data, a managed domain model, and FinOps discipline can ensure a stable ROI from AI implementation in the enterprise sector.
AI agent infrastructure optimization matrix: model-first vs. data-centric
| Comparison criteria | Model-First approach | Data-Centric approach (based on UnityBase) |
|---|---|---|
| Architectural focus | Prompt tuning and selecting larger models to compensate for data chaos. | Pre-structuring data at the Domain metadata level, using smaller models. |
| Token consumption | High (due to long contexts, repeated queries, and hallucinations). | Low (agent receives only relevant, filtered, and structured context). |
| Cost control (FinOps) | Post-factum analysis of the total cloud bill. | Transaction-based accounting (unit economics) allocated to specific business processes. |
| Data duplication | High (each agent builds its own ad-hoc connectors to sources). | Zero (unified semantic data layer, RLS/ACL, and API contracts). |
FAQ
How can I calculate the total cost of ownership (TCO) for AI agent infrastructure before development begins?
To calculate TCO, it is necessary to estimate the average number of input and output tokens per request, the frequency of calls, and the cost of auxiliary services (vector databases, caching). Modeling costs at the design stage is the foundation of the Cost Optimization principle recommended by the Microsoft Azure Well-Architected Framework.
Which FinOps tools are best suited for tracking LLM API costs?
The most effective approach is using a centralized API Gateway, which allows for adding custom metadata (tags) to each request. This enables the measurement of unit economics—the cost of each transaction linked to a business process or department, as required by the FinOps Framework.
How does the UnityBase platform help reduce the number of tokens in AI agent requests?
UnityBase structures corporate information through a unified domain model and automatically generated REST APIs. Instead of passing unstructured text arrays to an agent, the system transmits only precise, validated JSON context while respecting access rights (RLS/ACL), which minimizes prompt size and token costs.