Agentic AI Architectures — Designing the Right Model for Your Business Systems

Introduction

Financial institutions are weaving agentic AI into environments long dominated by structured, rule-based business systems. The key design question is no longer what’s the most advanced model? – but which configuration best fits our business architecture and operating model?

Architecturally, that means aligning where intelligence lives – and how it is governed – with the realities of single integrated platforms, modular estates, or best-of-breed ecosystems. The right approach is contextual. Centralisation is not automatically superior; the correct equilibrium depends on process ownership, data flows, and the institution’s architectural preferences.

The Core Systems You Build On

Before introducing any agentic capability, financial institutions must understand the environment it will inhabit. Most operate within one of three broad application architectures:

• Single Integrated platforms – unified front-to-back systems, common in finance, treasury and many asset managers.
• Functionally modular estates – distinct systems by functional area (risk, trading, operations, etc).
• Best-of-breed mosaics – specialist tools combined through interfaces or middleware.

Each foundation offers different strengths in coherence, agility, and control. Single integrated platforms bring lineage and workflow assurance; best-of-breed environments maximise capability depth but increase orchestration complexity. Understanding this base is essential before layering in agentic intelligence.

Three Agentic Models

Agentic systems can be arranged in three broad architectural patterns – each valid when matched appropriately to the core business applications it extends:

• Federated agentic model – agents operate within a function (e.g. trading, risk, or finance) using local data and metrics while adhering to shared governance.
• Hybrid agentic model – a dual-plane architecture combining local reasoning and execution with enterprise-level oversight for policy, lifecycle, and telemetry.
• Centralised agentic model – an enterprise hub concentrates orchestration, reasoning, and control.

Each model balances agility, assurance, and simplicity differently – and each can be architecturally correct when aligned to the systems it complements.

Matching Agentic Models to Business System Landscapes

Agentic AI can be deployed in different ways depending on the structure of an institution’s existing business systems.

The table below sets out nine viable configurations, formed by combining three system landscapes with three agentic operating models.

Choosing What Fits – A Practical Lens for FS Leaders

Think horses for courses. The right agentic architecture depends on the underlying system architecture it extends, and the level of control or autonomy required.

• Federated agentic approaches excel in domains where agility and iteration speed matter – such as trading strategies, operations, or client onboarding.
• Hybrid agentic setups work best across shared or interlocking processes – for instance, capital and liquidity management spanning Risk, Finance, and Treasury.
• Centralised agentic models provide uniformity and reduce system redundancy.

From Principles to Execution

Designing the right agentic architecture follows a sequence:

Understand the current application landscape. Map how business processes, systems, and data flows are structured today.

Determine the required autonomy and assurance levels. Align to regulatory and risk expectations.

Select the agentic architectural model that balances agility, control, and coherence for that specific context.

In Summary

Agentic AI doesn’t replace the institution’s core business systems – it complements and extends them. The important idea is alignment: deploying the agentic model that fits the function and the controls that fit the risk.