Realizing the potential of AI in finance is a multifaceted challenge spanning data and modeling, context and memory, trust and control, and orchestration at scale. Enterprise AI systems must understand finance-native structured data, retain the right context over long interactions, act safely across untrusted environments, and operate reliably at organizational scale.

The talk is aimed at researchers and engineers building agentic systems who are interested in how these capabilities behave under enterprise constraints. Attendees will leave with a framework for reasoning about the trade-offs between model structure, memory cost, trust enforcement, and orchestration, along with the failure modes we encountered in each.

In this talk, we will examine four interconnected capabilities: structured financial foundation models, progressive-disclosure memory, trust architectures, and multi-agent orchestration. Structured financial foundation models provide reusable representations of transactions, markets, entities, and relationships. Progressive-disclosure memory enables assistants to recover precise evidence from long histories while controlling inference cost. Trust architectures establish safeguards external to the agent as it accesses sensitive information, invokes tools, and operates across untrusted environments. Multi-agent orchestration supports production-scale AI through planning, policy enforcement, benchmarking, and harness optimization.

Drawing on research, platform development, and operational experience at JPMorgan Chase, we outline the challenges, approaches, and lessons associated with each capability. Together, these perspectives connect frontier AI research with the practical demands of building reliable AI at enterprise scale and outline the technical foundations for the next generation of AI in finance.