You are reviewing an architectural diagram for a Claude-powered travel-booking system. The diagram shows a top-level component that interprets user requests and three subordinate components that handle flights, hotels, and ground transportation. The top-level component is responsible for routing each request, sequencing the subordinate components, and reconciling their outputs into a final itinerary. The diagram also shows that each subordinate component has its own tool list and own short conversation history that is not shared with the others.
Which architectural pattern does this diagram most closely describe?

• A. A retrieval-augmented pattern, where the top-level component retrieves relevant context from the subordinate components' indexed data stores before generating each response.
• B. A manager and supervisor pattern with shared context, where the top-level agent and the subagents all share a single conversation history that grows as the request flows through the system.
• C. A pipeline pattern, where each component processes the user request in sequence and passes the full conversation history along with the request to the next component in the pipeline.
• D. A manager and supervisor pattern with isolated context per subagent, where the top-level agent coordinates specialized subagents that each maintain their own conversation history and tool list.

A teammate has asked how to extend Claude Code with a custom Skill that the team can invoke during sessions. The Skill consists of a set of instructions and a few support scripts the team wants Claude to be able to call when the Skill is loaded.
Where is the right place to define the Skill?

• A. Define the Skill in a Skills directory recognized by Claude Code, where Claude can discover and load it during sessions for the team's repositories.
• B. Define the Skill in a personal scratch directory on each developer's machine and load it manually before each Claude Code session that needs it.
• C. Define the Skill as a long inline instruction at the top of every CLAUDE.md file in the team's repositories so Claude has access to it on every session.
• D. Define the Skill inside the application's source code as a regular library module and call it from the application code instead of from Claude Code.

Your Claude application runs long agentic workflows where the agent makes many tool calls, and the conversation history grows quickly. After about 20 tool calls, you notice the agent's responses become less focused and sometimes ignore earlier task constraints.
How would you address this?

• A. Remove tool calling from the workflow entirely so the agent operates as a single text-generation step with no tool outputs accumulating in the context window.
• B. Restart the agent every five tool calls to prevent any drift, with the agent losing all task state at each restart point during the workflow.
• C. Apply context engineering techniques such as tool output pruning or compaction to keep the active task state visible while reducing the volume of older content.
• D. Increase the model's context window so the agent can hold every tool output at full detail across the entire workflow no matter how many tool calls it accumulates.

The product team has asked you to choose a Claude model for a new feature. The team has provided functional requirements but has not specified performance, cost, or quality targets. The team's product manager says, "Use whatever model gives us the best results." How would you respond?

• A. Choose a mid-tier model and ship the feature, because mid-tier models work for most use cases without specified targets.
• B. Choose the largest, highest-capability Claude model, on the grounds that "best results" is most likely to mean highest quality.
• C. Run every Claude model on a representative sample and pick whichever scores best on a generic benchmark.
• D. Ask the product team to specify quality, latency, and cost targets, then select the model whose tradeoffs best fit those targets.