Design a policy engine for deciding which tools an AI assistant may call in each context.

Instruction: Evaluate principal, resource, action, data sensitivity, destination, and approval requirements from trusted context.

Context: Build a policy decision point that can allow, deny, or require approval for a specific tool action and enforce that decision.

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I would represent the proposed action as a typed request: authenticated principal, tenant, tool version, target resource, intended mutation, and any external destination. The policy engine returns allow, deny, or require approval with a reason code and policy version.

Inputs such as user identity and data classification come from trusted...

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Your preparation path

Work through these questions in order. Read the answer aloud, then explain it in your own words.

1. Start with the foundations

Build the vocabulary and explain the core decisions.

  1. When should a model call a tool instead of answering from context? Free sample
  2. How would you explain MCP to a product engineer in one minute? Free sample
  3. What information should the orchestrator validate before executing a tool call? Free sample

2. Apply it to a real workflow

Practice diagnosis, validation, and everyday tradeoffs.

  1. Why do idempotency and retries matter more with AI-driven tool use? Free sample
  2. How do you keep permission boundaries clear when many tools share overlapping capabilities? Member answer
  3. A user revokes access to a connected app mid-session. How should the AI workflow behave? Member answer

3. Prepare for senior discussions

Explain failure boundaries, recovery, and production choices.

  1. Design a fallback strategy when tools are unavailable, degraded, or unauthorized. Member answer
  2. How do you implement MCP step-up authorization without losing scopes or retrying forever? Member answer
  3. An MCP request resumes after user confirmation. How would you validate its requestState? Member answer

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