Agent Lead

Job Description
What is the opportunity?
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.
What will you do?
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

What do you need to succeed?
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)

Additional Job Details
Address:
250 NICOLLET MALL:MINNEAPOLIS

City:
Minneapolis

Country:
United States of America

Work hours/week:
40

Employment Type:
Full time

Platform:
WEALTH MANAGEMENT

Job Type:
Regular

Pay Type:
Salaried

Posted Date:
2026-05-12

Application Deadline:
2026-07-03

Note

:

Applications will be accepted until 11:59 PM on the day prior to the application deadline date above
Our Employment Opportunities
At RBC, we are guided by living shared values of Client First, Integrity, Collaboration, Respect and Excellence and winning together as One RBC. We believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world. Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities. RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.
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RBC is presently inviting candidates to apply for this existing vacancy. Applying to this posting allows you to express your interest in this current career opportunity at RBC. Qualified applicants may be contacted to review their resume in more detail.

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