When Autonomous Agents Disagree: Architectural Governance, Time-to-Production, and APRA Compliance in Autonomous BFSI Operations

This executive roundtable is designed for C-1 Banking, Financial Services, and Insurance (BFSI) technology, risk, and operations leaders in Sydney. It addresses a major industry crisis: 74% of AI agents placed into production are decommissioned within months because they operate in isolation, lack system context, generate unpredictable costs, or fail strict regulatory audits.

While development teams rush toward autonomous execution, Australian financial institutions must operate under deterministic ledgers and strict APRA requirements. This session focuses on how enterprise leaders bridge the gap between AI creation and enterprise execution using the OutSystems Agentic Systems Platform—scaling delivery without accruing technical or governance debt.

3 Core Pillars of Discussion

1. Decision Rights & APRA CPS 230 / CPS 234 Compliance

• Focus: Establishing clear delegation boundaries between human authorization and autonomous agent execution, while satisfying the strict demands of both operational resilience (CPS 230) and information security (CPS 234).
• Platform Mechanism: Agent Workbench provides real-time observability, permission-aware AI retrieval, security guardrails, and audit trails for every agent reasoning path to ensure compliance across all regulatory gates.

2. The Post-Generation Bottleneck & Time-to-Production

• Focus: Shifting the measurement of AI success from lines of code generated to actual time-to-production. While AI coding tools can deliver output up to 100x faster, this acceleration often creates massive downstream bottlenecks and rework loops in testing, architecture review, and security assessments.
• Platform Mechanism: Shortening the queues after generation by validating intent before building. The platform combines automated testing, AI-assisted architecture reviews against the entire estate, and one-click deployment to move work rapidly from approved idea to secure, functioning software in users’ hands.

3. Legacy Modernization: Compressing Discovery & Grounding AI

• Focus: The dual role of AI in legacy environments. AI creates an urgent imperative to modernize, as legacy mainframes, monoliths, and closed workflows are completely closed to AI and pose severe regulatory risks due to a lack of audit trails, access controls, and resilience. Simultaneously, AI directly assists the modernization process by radically compressing the discovery phase—reading legacy code, extracting business rules, mapping data models, and generating first-cut replacements in a fraction of the time.
• Platform Mechanism: While AI accelerates code generation, it cannot solve complex cutover, integration, or risk sign-off challenges on its own. The OutSystems Enterprise Context Graph provides a live, queryable map of the organization’s existing apps, data, integrations, agents, and policies. This grounds AI coding tools (such as Mentor, AWS Transform, and Kiro) in the actual enterprise context, ensuring all generated changes land in a single, reviewable architectural model rather than creating untamed technical drift.

Make plans now to join this executive level discussion. You’ll come away with an actionable directive to establish a Production Agent Health Audit across your technology and risk functions to capture field verbatims on why existing agents stall, and implement centralized platform guardrails to scale autonomous capabilities safely.