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.
During testing, you observe that in extended exploration sessions (30+ minutes), the agent starts giving inconsistent answers about code structure it discussed earlier. Engineers report having to repeat context about modules they've already explored. What's the most effective approach to address this?

• A.Implement automatic context clearing every 15 minutes to ensure the agent starts with fresh, uncontaminated context.
• B.Switch to a higher-capacity model tier to provide more context window space for accumulated exploration data.
• C.Create summaries of all source files before exploration begins, loading only these compressed representations into context.
• D.Have the agent maintain a scratchpad file that records key findings, referencing it for subsequent questions.

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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.
Your codebase exploration tool stores session IDs to allow engineers to continue investigations across work sessions. An engineer spent an hour yesterday analyzing a legacy authentication module, building context about its architecture and dependencies. They want to continue today.
The session ID is valid, but version control shows 3 of the 12 files the agent previously read were modified overnight by a teammate's merge. What approach best balances efficiency and accuracy?

• A.Start fresh session to ensure the agent works with current codebase state without stale assumptions
• B.Resume the session and inform the agent which specific files changed for targeted re-analysis
• C.Resume the session and immediately have the agent re-read all 12 previously analyzed files
• D.Resume the session without informing the agent about the changed files

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An MCP tool processes financial refunds. A policy violation occurs because the requested amount exceeds the user's maximum allowed limit. What is the most architecturally sound way the tool should respond?

• A.Return a generic "Operation failed" string to avoid leaking internal policy limits.
• B.Throw a fatal system exception to halt execution.
• C.Return structured error metadata with isError: true, a human-readable context description of the policy violation, and isRetryable: false.
• D.Return an empty response payload so the agent can seamlessly try a different tool.

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A developer is designing a JSON schema to extract contract details from unstructured documents. Sometimes the 'termination_fee' is not mentioned in the source document. How should the schema be designed to prevent the model from fabricating a value?

• A.Set the field type to strictly number and add a system prompt forbidding hallucination.
• B.Use a PreToolUse hook to verify the field exists in the document before extraction.
• C.Design the field as optional or nullable, such as ["number , " " null" ].
• D.Set the tool_choice parameter to auto so the model can skip the tool call.

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Users report that final reports sometimes lack depth on specific subtopics. Investigation shows that the document analysis agent frequently identifies gaps - for instance, noting "the retrieved sources discuss API authentication but lack details on token refresh patterns" - but under the current strict pipeline, this insight isn't actionable since search has already completed. What's the most effective architectural change?

• A.Add a research planning agent before the search phase that decomposes topics into specific sub- questions.
• B.Have the analysis agent report specific gaps to the coordinator, which triggers targeted searches and re-invokes analysis until sufficient.
• C.Have the coordinator review analysis output for gap indicators and re-invoke search with gap- informed queries when gaps are detected.
• D.Have the synthesis agent attach confidence scores to each section and flag areas with insufficient coverage for manual review.

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