Agent Lead

Agent Lead
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. This is not a pure engineering role-it is a product-minded builder who shapes, validates, and scales agentic patterns that can be reused across Wealth Management.

The mandate is to move fast, prove value, and establish repeatable patterns, while aligning to enterprise architecture, risk, and AI governance standards.

Agentic Strategy & Use Case Qualification

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

Agent Design, Build & Rapid Experimentation

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

Product Ownership & Lifecycle Accountability

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

Maintain a portfolio of agentic capabilities with clear value articulation and reuse potential

Enterprise Alignment & Governance Integration

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

Ensure all agentic solutions align to:

Model risk governance

Data privacy and security requirements

AI explainability and control frameworks

Agent Ecosystem & Standards Definition

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

Contribute to evolving enterprise agent standards through applied learnings

People Leadership & Capability Building

Manage and develop Context Engineers / Prompt Engineers

Establish best practices in:

Context design and retrieval strategies

Prompt engineering and agent behavior tuning

Build a culture of experimentation, accountability, and pragmatic problem solving

Field Engagement & Adoption

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

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

Strong judgment in when to use AI vs. when not to

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

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

What's in it for you?

Direct ownership of one of the most strategically important capabilities in RBC Wealth Management-agentic AI

Opportunity to define how AI agents fundamentally reshape advisor productivity and client engagement

High visibility across business, technology, and executive leadership

Ability to operate in a true "build, test, learn" environment with real‑world impact

Leadership of a next‑generation capability (context engineering + agent orchestration)

Influence on enterprise‑wide agent standards and architecture direction

The good‑faith expected salary range for the above position is $100,000 - $170,000 depending on factors including but not limited to the candidate's experience, skills, registration status; market conditions; and business needs. This salary range does not include other elements of total compensation, including a discretionary bonus and benefits such as a 401(k) program with company‑matching contributions; health, dental, vision, life and disability insurance; and paid time‑off plan.

RBC's compensation philosophy and principles recognize the importance of a highly qualified global workforce and plays a critical role in attracting, engaging and retaining talent that:

Drives RBC's high performance culture

Enables collective achievement of our strategic goals

Generates sustainable shareholder returns and above market shareholder value

Job Skills: Agentic AI, Artificial Intelligence Technologies, Business Process Flows, Business Process Improvements, Commercial Acumen, Data Science, Decision Making, Enterprise Architecture (EA), Enterprise Architecture Framework, Generative AI, Generative AI Agents, Lean Business Processes, Machine Learning (ML), Natural Language Processing (NLP), Predictive Analytics, Python (Programming Language)

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