AI & Technical question
A large enterprise wants to extend Sierra’s agent with custom business logic, internal data sources, and policy guardrails. How would you define the SDK architecture and API surface so developers can customize behavior deeply without making the platform unreliable, insecure, or hard to adopt?
- Sierra
- AI & Technical
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
Start a mock interview on this question · Mock interview from a job description
What this question tests
Tests defining an SDK architecture and API surface that allows deep customization for enterprise developers without compromising platform reliability, security, or ease of adoption.
How to approach it
- Define extension points explicitly: custom business logic via defined hook functions at specific points in the conversation flow, custom data sources via a standardized connector interface, and policy guardrails via a declarative rules configuration.
- Keep the core conversation engine, safety enforcement, and escalation logic outside of what developers can override, so customization happens around a protected, reliable core rather than replacing it.
- Sandbox custom logic execution so a bug or slow custom hook can't take down the underlying platform or degrade other customers' agents, using timeouts and resource limits.
- Validate custom data source connectors against the same permission and data-handling requirements as first-party connectors, so deep customization doesn't create a security gap.
- Provide a declarative, not fully imperative, interface for policy guardrails where possible, since a config-based rules system is easier to audit and less error-prone than arbitrary code for security-critical behavior.
- Offer a staged customization path, simple hook configuration for most developers, with an advanced custom-code option gated behind additional review, balancing power against platform reliability.
What a strong answer includes
- Protects the core safety and escalation logic from being overridden, keeping customization additive around a reliable, non-negotiable core.
- Sandboxes custom logic explicitly with timeouts and resource limits, preventing one customer's custom code from degrading the shared platform.
- Prefers declarative configuration over arbitrary code for security-critical policy guardrails, since it's more auditable and harder to misconfigure dangerously.
- Offers a staged path from simple configuration to advanced custom code, matching the review rigor to the risk level of what's being customized.
Common mistakes
- Allowing developers to override core safety or escalation logic directly, undermining platform-wide reliability guarantees.
- Skipping sandboxing for custom logic, risking one customer's code degrading shared platform performance.
- Using arbitrary code for security-critical policy configuration instead of a more auditable declarative interface.
Likely follow-up questions
- How would you review or audit custom logic before it goes live for an enterprise customer?
- What would you do if a customer's custom data source connector doesn't meet your permission requirements?
More ai & technical questions
- Design a QA system that keeps Sierra's branded agents on-brand and accurate.Sierra · AI & Technical · Hard
- Sierra needs a platform layer that lets product teams ship new AI agent experiences quickly without each team re-solving infrastructure. Design the core abstractions you would standardize across compute, storage, orchestration, and networking. What would you expose as platform primitives versus hide behind managed interfaces, and how would you ensure the design can meet high-concurrency, low-latency, enterprise uptime requirements?Sierra · AI & Technical · Hard
- A deployed Sierra agent resolves most conversations but fails on a small set of high-stakes cases. How would you determine whether to invest first in model changes, better retrieval/context, workflow constraints, or earlier human handoff?Sierra · AI & Technical · Hard
- During a peak support window, a live Sierra agent starts giving incorrect answers across many conversations. How would you contain the issue, decide whether to narrow or disable automation, inspect whether the failure comes from prompts, retrieval, tool calls, or upstream data, and define the permanent fix?Sierra · AI & Technical · Hard
- A customer reports that Sierra’s agent performs well in English but degrades in Spanish when users use regional slang, register shifts, or code-switching. How would you diagnose whether the issue is prompt design, retrieval/context quality, model limitations, or evaluation gaps, and how would you prioritize fixes with engineering?Sierra · AI & Technical · Hard
- Before launching a new AI workflow for a high-volume support use case, what quality bar would you set? Define the offline and online eval framework, launch criteria, and post-launch monitors you would use to measure task success, reliability, safety and groundedness, latency, fallback behavior, and customer trust at scale.Sierra · AI & Technical · Hard
More questions from Sierra
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture