AI Is Changing the Game: Building Modular, AI-Ready Platforms on Top of Legacy Systems

AI is no longer a future topic—it is actively reshaping expectations inside organizations. Domain and business teams can now prototype new rules, validations, and analytical logic themselves, often within days. While this accelerates innovation, it puts enormous pressure on existing IT architectures, especially in environments dominated by legacy systems and monolithic platforms.

This talk explores how software architecture must evolve to absorb this pressure instead of breaking under it.

Rather than embedding AI capabilities directly into legacy systems, the presented approach introduces a modular, AI-ready platform built around independent, stateless apps orchestrated by a central control layer. These apps can represent classical reporting logic, risk calculations, or AI agents, all treated as first-class architectural components.

The talk is highly relevant for the PyCon track “Programming, Software Engineering & Testing”, because it demonstrates how to design, orchestrate, and integrate AI-driven workflows in complex Python-based platforms. The central control layer, implemented using Python and optionally Django, provides workflow orchestration, security, tenant management, and self-service registration of new components. This allows domain teams to deploy AI agents or agents written with the help of AI within days, while IT retains governance, auditability, and operational stability.

By showing how AI-driven pressure can be turned into an architectural advantage, the talk provides patterns and practical lessons that apply far beyond finance, making it relevant for any domain dealing with legacy systems, modular design, and AI integration.

Architectural Concepts Covered

The talk introduces the key architectural principles behind the platform:

• Independent, stateless apps that declare their data needs and outputs but remain unaware of infrastructure, environments, or other apps
• Strict separation of concerns between domain logic, orchestration, persistence, and presentation
• Technology-indifferent design, allowing apps to run on different databases, reporting tools, or compute backends
• Parallel and distributed execution as a default, not an optimization

This architecture allows legacy systems to coexist with modern components instead of blocking innovation.

The Control Layer as an Enabler for AI

A central part of the talk is the control layer that orchestrates all components. Implemented using Python and optionally Django, this layer is responsible for:

• workflow orchestration and dependency management
• authentication, authorization, and tenant isolation
• self-service registration of apps and AI agents
• resource allocation, monitoring, and auditability

Django is not used as a traditional CRUD backend, but as governance infrastructure: providing APIs, admin and self-service portals, and security mechanisms that allow fast innovation without losing control.

Example: Integrating an AI Agent into a Regulated Platform

A concrete example demonstrates the architecture in action: integrating an AI agent for e.g. anomaly detection in regulatory reporting.

The example walks through:

• developing the agent as an independent, containerized app
• registering it via standardized APIs
• declaring required data and produced results
• orchestrating it within existing workflows
• testing, monitoring, and scaling it without touching legacy systems

This shows how new AI capabilities can be deployed within days while maintaining stability and compliance.

Why This Matters Beyond Finance

While the example comes from regulatory reporting, the patterns discussed apply to many domains facing similar challenges: data-heavy systems, long-lived platforms, and increasing pressure to integrate AI safely.

The talk concludes with lessons learned and architectural patterns that help future-proof systems as AI continues to raise the bar for flexibility, speed, and modularity.