Open Source in Finance Forum London 2026

Large Language Models (LLMs) offer powerful reasoning and automation capabilities, but their probabilistic nature conflicts with the determinism, explainability and auditability required in financial services. This session presents a practical architectural pattern for agentic AI: LLMs as bounded cognitive components orchestrated by BPM-based workflow engines, such as Fluxnova.
Rather than deploying autonomous agents as opaque black boxes, this approach embeds LLMs within explicit and versioned business process models. BPM orchestration governs control flow, approvals, escalation paths, exception handling, and audit checkpoints, while LLMs are invoked for well-scoped tasks such as document extraction, classification, summarization and recommendation generation. The result is agentic behavior that is powerful yet constrained, flexible and governed.
Every outcome can be traced through a BPM execution graph, showing inputs, policies applied, LLM interactions, and human-in-the-loop decisions. We illustrate this pattern in the context of a financial use case to demonstrate how institutions can safely scale agentic AI while meeting regulatory, risk, and audit expectations.