1. Agentic Strategy and Global Adoption
• Define and execute the multiyear strategy and roadmap for agentic product and engineering across the company’s global technology organization.
• Establish adoption objectives by engineering function, product domain, geography, technology stack, and maturity, moving teams from controlled experimentation to governed production use and agentic-first practices.
• Partner with global product and engineering leaders to embed agentic capabilities into delivery models, processes, organizational structures, and accountabilities.
• Identify and remove technical, organizational, cultural, talent, process, and governance barriers to adoption.
• Establish executive governance and reporting covering adoption, investment, delivery outcomes, risk, cost, and realized business value.
2. Agentic-Native Engineering Organization
• Build and lead an initial team of approximately 10 to 15 engineers, architects, platform specialists, and agentic-development leaders, scaling it into a larger global engineering organization as value and demand grow.
• Operate the team through an agentic-native model in which approved agents and workflows perform software design, development, testing, documentation, deployment, monitoring, maintenance, and modernization.
• Ensure engineers primarily direct, orchestrate, supervise, validate, and optimize agents rather than rely on conventional manual software-development practices.
• Deliver high-priority enterprise software, reusable components, platform capabilities, and modernization initiatives through this model.
• Establish engagement, prioritization, delivery, talent, and leadership models, and codify successful practices into reusable standards, playbooks, architectures, agents, skills, and accelerators.
3. Agentic Delivery Lifecycle
• Own the design, implementation, governance, and continuous evolution of the company-wide ADLC.
• Embed agentic capabilities throughout requirements, architecture, coding, review, testing, security validation, documentation, release, deployment, production operations, incident response, and modernization.
• Define reusable patterns, control gates, certification, production-readiness standards, and risk-based requirements for human supervision, validation, approval, and intervention.
• Integrate the ADLC with enterprise source-code management, CI/CD, testing, security, observability, change-management, and production-operations platforms.
• Establish versioning, auditability, rollback, monitoring, incident-management, and lifecycle controls without compromising quality, resilience, maintainability, security, or regulatory compliance.
4. Agentic Platforms and Developer Experience
• Define the requirements and target architecture for enterprise-grade agentic development and execution platforms, partnering with Platform Engineering, Enterprise Architecture, Security, and engineering leaders on implementation.
• Lead adoption and integration of approved technologies such as OpenAI Codex, Anthropic Claude Code, GitHub Copilot, and comparable capabilities.
• Provide secure, reliable, self-service access to approved models, tools, execution environments, enterprise data, repositories, APIs, golden paths, and reusable platform services.
• Define requirements for model routing, context and memory, identity, secrets, privileged access, auditability, observability, availability, scalability, and disaster recovery; prevent fragmented tooling and ungoverned deployments.
5. Agents, Skills, and MCP Ecosystem
• Lead development of reusable enterprise agents, specialized skills, workflows, orchestration capabilities, and Model Context Protocol (MCP) services.
• Establish an enterprise registry and standards for approved agents, skills, prompts, tools, MCP servers, context, memory, delegation, testing, versioning, ownership, and retirement.
• Develop secure MCP servers and comparable integrations connecting models with enterprise applications, engineering platforms, data environments, operational tools, and core fintech APIs.
• Establish certification, access, and reuse requirements that promote interoperability while preventing duplication, inconsistent practices, and uncontrolled agent proliferation.
6. Engineering Adoption and Transformation
• Establish an acceleration capability that works directly with product and engineering organizations to identify and implement high-value agentic use cases.
• Deploy embedded engineers into priority domains and lead lighthouse implementations that demonstrate value, transfer knowledge, and create sustainable local capability.
• Develop training, technical academies, certifications, communities of practice, engineering forums, and a global network of agentic engineering champions.
• Partner with engineering management to redefine roles, skills, team structures, workflows, career paths, and capacity assumptions as adoption matures.
• Create implementation playbooks and change programs that support responsible experimentation, build confidence, address resistance, and sustain adoption across cultures and geographies.
7. Governance, Security, and Production Assurance
• Establish governance for ownership, approval, production access, operation, monitoring, and retirement of agents and agentic engineering capabilities.
• Implement controls addressing data leakage, hallucination, prompt injection, insecure code generation, model misuse, unauthorized tool execution, intellectual-property exposure, and excessive autonomy.
• Ensure production agents and agent-generated software have accountable owners, appropriate testing, audit trails, monitoring, rollback capabilities, and incident-management processes.
• Partner with Information Security, Legal, Privacy, Risk, Compliance, and Internal Audit to meet regulatory and responsible-AI requirements, including clear exception, escalation, remediation, and risk-acceptance processes.
8. Value Realization and Cost Management
• Define baselines, targets, dashboards, and executive reporting for productivity, cycle time, release frequency, quality, defect leakage, change-failure rate, reliability, modernization velocity, and developer experience.
• Measure adoption and performance by team, geography, domain, workflow, and maturity, and compare the agentic-native organization with conventional delivery approaches.
• Establish transparency and controls for token, model, licensing, infrastructure, and platform costs; optimize routing, context, caching, prompts, and platform utilization.
• Remediate, consolidate, or retire underperforming and high-risk use cases, translating productivity gains into greater capacity, faster delivery, improved outcomes, and reduced cost.