← Bank of America Interview Insights
I asked this expecting to catch someone out, and the answer I got was something like 'if I were doing healthcare or aerospace work I'd probably look closely at code review, maybe even write the checks myself.' My first reaction was that this was a non-answer from someone used to shipping PoCs.
Acknowledge that in safety-critical systems, AI-generated code must be treated as untrusted and subjected to rigorous verification. Emphasize a defense-in-depth strategy combining formal methods, extensive testing, and human oversight, while being transparent about the limitations of AI. Highlight the importance of traceability, independent verification, and regulatory compliance.
Pro tip: Reference specific safety standards like DO-178C or ISO 26262 to demonstrate domain knowledge, and stress that AI should augment, not replace, human judgment in safety-critical contexts.
Implement formal verification, static analysis, and exhaustive testing to mathematically prove correctness and uncover edge cases. Use multiple independent verification tools to avoid common-mode failures.
Require domain experts to manually review all AI-generated code, with a focus on safety-critical paths. Use pair programming or independent review boards to catch subtle errors.
Follow standards like DO-178C or ISO 26262, which mandate traceability, requirements-based testing, and configuration management. Integrate AI-generated code into this lifecycle with additional scrutiny.
Add runtime checks, redundancy, and fail-safe mechanisms to detect and mitigate failures. Use diverse redundancy (e.g., N-version programming) to avoid correlated errors.
Monitor AI performance, retrain with safety-focused datasets, and conduct regular audits. Establish a feedback loop to update processes as AI capabilities evolve.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
This was the core of my new interview format and one candidate straight up refused to engage with it the traditional way.
Frame your answer around a risk-based review process that starts with understanding the business impact and the agent's intent, then systematically verifies correctness, security, and compliance. Emphasize collaboration with domain experts and a feedback loop to improve the agent, while highlighting the need for auditability in a regulated banking environment.
Pro tip: In banking, always assume the agent's output could introduce subtle compliance or security flaws; treat the PR as untrusted until proven otherwise, and document every decision for audit purposes.
Review the PR description, linked tickets, and agent logs to grasp the intended business logic change and why the agent made it. Identify the critical business logic touched and the potential impact on customers, revenue, and compliance.
Focus on high-risk areas: data handling, authentication, authorization, error handling, and edge cases. Check for security vulnerabilities, logic errors, and deviations from coding standards, using static analysis tools and manual inspection.
Cross-check the changes against business requirements, regulatory rules (e.g., GDPR, SOX, PCI-DSS), and internal policies. Ensure the agent's output aligns with expected behavior and doesn't introduce compliance risks.
If issues are found, provide clear, actionable feedback to the agent's maintainers or the team, prioritizing fixes by severity. Pair with domain experts to resolve ambiguities and ensure the fix addresses root causes, not just symptoms.
After fixes, verify the changes with tests and a re-review. Document lessons learned and feed them back into the agent's training or guardrails to prevent similar issues in future PRs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Nobody asked me this directly but it's what the whole conversation kept circling back to.
Describe a concrete, layered workflow that treats AI-generated code as untrusted input, emphasizing automated gates (tests, linters, static analysis) and human review of high-risk areas. Highlight how you balance velocity with safety, especially in a regulated banking environment, and mention specific tools and practices you use.
Pro tip: Emphasize that you never let AI-generated code bypass the same quality gates as human-written code, and that you use AI itself to help review and test AI-generated code—this shows maturity and scalability.
Define which parts of the codebase AI can generate (e.g., boilerplate, tests, docs) and which require human authorship (e.g., security-critical, core business logic). Establish guidelines for acceptable AI use and required documentation.
Enforce mandatory automated checks: unit/integration tests, linters, static analysis, security scanners, and type checkers. Ensure these run in CI/CD and block merges on failure, regardless of code origin.
Use AI tools to review AI-generated code for common issues (e.g., hallucinations, insecure patterns) and to generate additional tests. Combine with human review focused on architecture, edge cases, and compliance.
Track metrics like defect density, review time, and test coverage for AI-generated vs. human-written code. Use findings to refine prompts, guidelines, and tooling continuously.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.