Agent Lead

About the role

The Agent Lead sits at the forefront of transforming Financial Advisor productivity through agentic AI workflows—bridging business problems with intelligent automation. This role operates in a high-ambiguity, rapid experimentation environment, identifying where agent-based approaches can unlock value—and equally, where they should not be applied. You will define how vendor agents (e.g., CRM-native), enterprise frameworks, and internally developed agents coexist and interoperate within a governed ecosystem.

Responsibilities

• Partner directly with Financial Advisors, field leadership, and business stakeholders (“side-of-desk”) to identify high-value workflow opportunities.
• Evaluate when to apply agentic AI vs. deterministic automation vs. no automation, with the authority to say “this is not an AI problem.”
• Define and prioritize agentic use cases aligned to advisor productivity, client engagement, and operational efficiency.
• Lead rapid POC development cycles (fail fast / scale fast) for agentic workflows.
• Design multi-agent interactions across: Vendor agents (e.g., CRM/Agentforce), Enterprise agents (shared services / platforms), Native/internal agents (event-driven, workflow-specific).
• Establish reusable agent design patterns (prompting, orchestration, memory, tool usage, escalation paths).
• Partner with AI Engineering to validate feasibility, performance, and scalability.
• Act as Product Owner for agentic workflows—owning use case shaping through validated solution patterns.
• Ensure solutions are not “built and dropped” by defining success metrics and adoption criteria, driving iteration based on advisor feedback and usage telemetry, and maintaining a portfolio of agentic capabilities with clear value articulation and reuse potential.
• Engage with enterprise stakeholders (e.g., Borealis / enterprise AI, architecture, and platform teams) to align with approved agentic frameworks and standards, leverage existing enterprise capabilities before building net new, and ensure all agentic solutions align to: Model risk governance, Data privacy and security requirements, AI explainability and control frameworks.
• Define how agent ecosystems operate within RBC Wealth Management, including interaction models between vendor, enterprise, and native agents, guardrails for agent autonomy and decisioning, cost-efficiency and performance considerations, and contribute to evolving enterprise agent standards through applied learnings.
• Manage and develop Context Engineers / Prompt Engineers, establishing best practices in context design and retrieval strategies, prompt engineering and agent behavior tuning, and build a culture of experimentation, accountability, and pragmatic problem solving.
• Travel (~25%) to branches and field locations to observe advisor workflows firsthand, identify friction points and real-world opportunities for agents, validate usability and adoption of agentic solutions.

Requirements

Must-have:

• Proven experience building and deploying agentic AI solutions (multi-agent systems, orchestration frameworks, tool-using agents)
• Strong understanding of agent frameworks and architectures (e.g., orchestration layers, memory models, tool integration, event-driven agents)
• Demonstrated ability to operate as a builder + product owner hybrid
• Experience working across business, engineering, and enterprise governance functions
• Ability to rapidly prototype (POCs) and iterate based on real user feedback
• Experience designing workflow-driven automation (not just models)
• Leadership experience managing technical talent (e.g., prompt/context engineers)
• Excellent stakeholder engagement skills—comfortable working “side-of-desk” with advisors and executives

Qualifications

Nice to have:

• Experience in Wealth Management / Financial Services, particularly advisor workflows
• Familiarity with CRM-based agent platforms (e.g., Salesforce Agentforce)
• Exposure to event-driven architectures and real-time data integration
• Understanding of AI risk, model governance, and explainability frameworks
• Experience integrating with enterprise AI platforms (e.g., internal AI platforms, cloud AI services)
• Background in human-centered design or workflow optimization