You are building a customer support resolution agent using the Claude Agent SDK. The agent handles high- ambiguity requests like returns, billing disputes, and account issues. It has access to your backend systems through custom Model Context Protocol (MCP) tools (get_customer, lookup_order, process_refund, escalate_to_human). Your target is 80%+ first-contact resolution while knowing when to escalate.
A customer returns 4 hours after their initial session about the same billing dispute. The previous 32-turn session contains lookup_order results showing "Status: PENDING, Expected resolution: 24-48 hours." In testing, you observe that when resuming sessions with stale tool results, the agent often references the outdated data in responses (e.g., "I see your refund is still being processed") even after subsequent fresh tool calls return different information.
What approach most reliably handles returning customers?

• A.Resume with full history and configure the agent to automatically re-call all previously used tools at session start to ensure data freshness.
• B.Resume with full history and add a system prompt instruction telling the agent to always prefer the most recent tool results when multiple calls to the same tool exist in context.
• C.Resume with full history but filter out previous tool_result messages before resuming, keeping only the human/assistant turns so the agent must re-fetch needed data.
• D.Start a new session, inject a structured summary of the previous interaction (issue type, actions taken, resolution status), then make fresh tool calls before engaging.

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Production monitoring shows that the research phase takes longer than expected. Analysis reveals that the coordinator invokes the web-search subagent, waits for its response, and then invokes the document-analysis subagent. These tasks are independent; neither requires the other's output. How should you modify the system to run these subagents concurrently?

• A.Structure the coordinator to emit both Agent tool calls--for web search and document analysis--in a single response message instead of separate conversation turns.
• B.Switch both subagents from a Sonnet-tier model to a Haiku-tier model to reduce their individual execution times.
• C.Add instructions explaining the performance benefits of parallel execution and request that the coordinator invoke both subagents simultaneously.
• D.Create an asynchronous orchestration layer that launches parallel threads, each running a separate coordinator-subagent pair, and then aggregates the results.

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Your pipeline runs:
PROMPT= " You are a code reviewer. "
PROMPT= " $PROMPT Analyze the provided diff "
PROMPT= " $PROMPT for bugs, security issues, "
PROMPT= " $PROMPT and style violations. "
claude -p \
--dangerously-skip-permissions \
--system-prompt " $PROMPT " < diff.txt
The reviews complete and return feedback, but Claude comments only on the piped diff-it never reads surrounding files in the checked-out repository to understand broader context, even when the diff modifies a function called by many other modules. Which change to the invocation will cause Claude to read related repository files while still applying your custom review instructions?

• A.Keep --system-prompt and add --allowedTools " Read, Glob, Grep " because non-interactive -p mode otherwise disables filesystem tools.
• B.Replace --system-prompt with --append-system-prompt so the review instructions are added to Claude Code's default prompt instead of overwriting its built-in file-reading and code-navigation guidance.
• C.Remove --system-prompt entirely and place the review instructions in a root-level CLAUDE.md because --system-prompt is incompatible with tool use under -p.
• D.Stop piping the diff through standard input and embed it in the prompt string so Claude Code treats the invocation as an agentic session rather than a stream-processing operation.

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You are building developer-productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools-Read, Write, Bash, Grep, and Glob-and integrates with Model Context Protocol (MCP) servers.
An engineer who recently joined the team asks the agent to explain the authentication and authorization architecture before making security improvements. The codebase contains more than 800 files across multiple services.
What exploration strategy will most effectively build understanding while respecting context limits?

• A.Launch parallel subagents to explore every service simultaneously, and then synthesize their findings into an architectural overview.
• B.Read all files containing auth , login , permission , or token in their filenames or contents.
• C.Read all CLAUDE.md and README files first, and then ask the engineer to identify the 10-15 most important authentication files.
• D.Use Grep to locate authentication entry points, read those files, and then follow imports and function calls incrementally to map the authentication flow.

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You are building developer productivity tools using the Claude Agent SDK. The agent helps engineers explore unfamiliar codebases, understand legacy systems, generate boilerplate code, and automate repetitive tasks. It uses the built-in tools (Read, Write, Bash, Grep, Glob) and integrates with Model Context Protocol (MCP) servers.
An engineer's exploration subagent spent 30 minutes analyzing a legacy payment system, reading 47 files and documenting data flows. The session was interrupted when the engineer's connection dropped. While away, a teammate merged a PR that renamed two utility functions.
The engineer wants to continue the same exploration.
What's the most effective approach?

• A.Launch a fresh subagent with a summary of prior findings.
• B.Resume the subagent from its previous transcript without mentioning the changes--the architecture understanding remains valid.
• C.Resume the subagent from its previous transcript and inform it about the renamed functions.
• D.Launch a fresh subagent and include the prior transcript in the initial prompt for context.

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