NLB Scales AI Agents with Enterprise Agentic AI Platform
AI agents at scale, under control. NLB deployed an enterprise Agentic AI platform to keep security, compliance, and costs in check.
months to AI platform and first use case productization
lower time and cost to implement AI use cases
data security, auditability and traceability
Challenge
Scaling Internal AI Agents Under Bank-Grade Controls
Slovenia is one of Europe’s fastest AI adopters, and NLB is taking a lead in banking. The bank recognized that GenAI and AI agents deliver real value: processing large volumes of documents, automating the gathering of key facts for credit and compliance, and supporting faster employee decisions.
Therefore, the question was never whether to scale AI agents, but how to do it responsibly, under control, and following future-facing best practices, while enabling teams to act.
Without a standardized Agentic AI platform, scaling agents in such a regulated environment could quickly introduce friction and risks, including:
- Shadow IT: Unapproved AI tools and agents operating outside governance, creating security and compliance blind spots
- IT overload: Teams buried in ad-hoc deployments and compliance checks
- Agent sprawl: Uncontrolled proliferation leading to inconsistent outputs
- Unpredictable costs: Spending without clear control
NLB uses cloud GenAI services and platforms, but these are industry‑general. The bank needed a bank‑grade control layer above any model or provider, including identity‑based approvals, policy enforcement, audit trails, and spend caps, so every agent follows the same path from idea to production while preserving model choice.
Solution
Agentic AI Platform with Centralized Agent Governance. Deployed in 5 Months.
NLB partnered with Adastra to build a centralized Agentic AI platform that standardizes how internal AI agents are designed, approved, and operate across the bank. Within 5 months, the bank deployed both the platform and the first AI use case into production.
The platform enables NLB to:
- Centralize agent design, approval, and operations with role-based access and full traceability
- Accelerate high-value internal use cases with reliable, explainable outputs
- Prevent agent sprawl and IT overload through consistent policies and environment separation
- Keep costs predictable via monitoring, rate limits, and spend caps
- Enable fast, department-by-department rollout while avoiding vendor lock-in
By integrating Adastra AGate, an AI mission control platform providing visibility, control, and cost management across AI models and agents, the system enforces consistent policies, controlled system access, and traceability.
Core Agentic AI platform capabilities:
- Controlled agent autonomy: Agents operate within clearly defined boundaries aligned to sensitive banking workflows.
- Identity-based access and approvals: Integration with Entra ID ensures accountability for who builds, approves, and uses agents.
- Cost visibility and observability: Usage, requests, and spending are tracked via an admin portal and Grafana dashboards.
- Separated environments: Dedicated environments for experimentation, testing, and production support safe iteration.
- Vendor-agnostic architecture: Support for Azure and AWS reduces dependency risk and increases long-term flexibility.
Impact
A Scalable and Repeatable Model for Safe and Predictable Agentic AI Adoption
Rather than scaling individual use cases in isolation, the bank now has a repeatable model for deploying AI agents across departments without re-engineering, fragmented controls, or unpredictable costs.
As adoption expands, the platform enables:
- Faster internal adoption: Teams can launch new AI agents department by department using the same controlled process.
- Reduced risk of errors: Role-based access and guardrails prevent mistakes in sensitive workflows like AML checks or credit approval.
- Predictable costs: Monitoring, rate limits, and spend caps make AI deployment financially manageable.
- Reusable blueprint: Policies, testing paths, and approval workflows can be applied to future use cases, accelerating enterprise-wide adoption.
Resulting in:
- Maximum data security and protection against shadow IT risks
- 80% reduction in AI use case implementation time and cost required
- Complete auditability and traceability
This enables NLB to scale GenAI and AI agents safely and with control, addressing the exact adoption and scaling challenges that cause many enterprise GenAI programs to stall.
