Ning Wang
Homeworld Educational Resources, R&D Director
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Ning has a PhD in Sustainable Earth Systems Science. His research spans applications such as climate systems, Earth systems, scientific data analysis, workforce intelligence, and public decision-making. A central theme of my work is the design of Multiple External Representations (MERs) to help people better understand complex information and make more informed decisions.
Durable Agentic AI Workflows with Temporal: Moving AI Agents from Experimentation to Production
Agentic AI systems are easy to prototype but difficult to operate in production. While LLMs have improved reasoning capabilities, the primary challenge has shifted from model intelligence to production engineering. Long-running execution, external tool invocation, human approvals, failure recovery, and governance become critical once AI agents interact with enterprise systems.
A typical AI agent reasons with an LLM, invokes external tools through MCP, and pauses for human approval before executing high-impact actions. In production, APIs fail, LLM providers enforce rate limits, workers restart, and workflows lose execution state. Without durable orchestration, workflows replay previous steps or duplicate side-effecting operations.
This session demonstrates how Temporal enables durable workflow orchestration through deterministic execution, workflow persistence, retries, Saga compensation, and asynchronous approvals. Drawing on production experience, I will present a reusable architecture for orchestrating LLMs, MCP tools, enterprise services, and governance checkpoints. Attendees will gain practical patterns for moving Agentic AI from experimentation to production.
Governance as Simulation: Evaluating AI Governance Risks and Mitigations with Agent-Based Modeling
Financial institutions increasingly need to evaluate agentic AI systems before production, but AI governance frameworks are usually applied as static checklists, which are harder to envision how the AI governance framework would be applied in real financial institutional environment.
This talk introduces a prototype agent-based modeling environment that translates FINOS AI Governance Framework risks and mitigations into configurable simulation experiments. Users can define a financial-service workflow, select AI agents and human roles, enter deployment context such as model version, hardware, software, data access, orchestration, and approval rules, then choose risks and mitigation controls to test. The simulator runs counterfactual scenarios, such as data drift, authorization bypass, model misalignment, infrastructure failure, or compromised tools, and compares outcomes including policy violations, detection time, operational delay, human workload, customer harm, and resilience. The goal is to demonstrate how “governance as simulation” could complement governance as documentation and governance as code, while creating a foundation for a future open-source FINOS project.
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