You are building a multi-agent research system using the Claude Agent SDK. A coordinator agent delegates to specialized subagents: one searches the web, one analyzes documents, one synthesizes findings, and one generates reports. The system researches topics and produces comprehensive, cited reports.
A user is expanding the research system beyond its single web search agent by adding specialized data sources. They add a financial API agent that returns structured JSON with revenue, margins, and growth rates; a news monitoring agent that returns prose summaries of recent developments; and a patent analysis agent that returns structured lists of technology areas. The synthesis agent combines these into executive briefings. Currently, it converts everything to bullet points, causing financial comparisons to lose tabular clarity and news summaries to lose narrative flow. What change would most improve briefing quality?

• A. Add a format conversion layer between subagents and synthesis that transforms all outputs to a common intermediate representation
• B. Update the synthesis agent to render each content type appropriately - financial data as tables, news as prose
• C. Standardize all subagent outputs to JSON with fields for claim, evidence, source, and confidence
• D. Standardize all subagent outputs to prose summaries with inline citations

You are using Claude Code to accelerate software development. Your team uses it for code generation, refactoring, debugging, and documentation. You need to integrate it into your development workflow with custom slash commands, CLAUDE.md configurations, and understand when to use plan mode vs direct execution.
You've documented API error handling conventions in a CLAUDE.md file at your project root, specifying that endpoint handlers should use a custom ApiError class. After several sessions, you notice Claude Code sometimes follows these conventions and sometimes uses generic try/catch blocks with string messages. The inconsistency appears random across different coding sessions. What's the most efficient first diagnostic step?

• A. Search for conflicting instructions in ~/.claude/CLauDe.md or ~/.claude/rules/ that might override your project conventions.
• B. Add a more detailed code examples to your CLAUDE.md showing the exact ApiError usage pattern for different endpoint types.
• C. Create path-specific rules in .claude/rules/handlers.md with YAML frontmatter scoping the error handling instructions to your API handler files.
• D. Run /memory to check which memory files are loaded and verify your CLAUDE.md is included.

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 agent has analyzed a complex service module -reading 23 source files, tracing request flows, and identifying error handling patterns. A developer wants to compare two testing strategies before committing to one: end-to-end tests with mocked external services vs. snapshot tests capturing expected outputs. They need to independently develop both approaches to evaluate trade-offs. How should you manage the sessions?

• A. Continue in the original session, developing end-to-end tests first, then snapshot tests sequentially.
• B. Start two fresh sessions, having each re-read the relevant source files before beginning.
• C. Export the analysis session's key findings to a file, then create two new sessions that reference this file.
• D. Resume the analysis session with fork_session enabled, creating a separate branch for each testing strategy.

A customer returns 4 hours after the 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 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.
• B. 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.
• C. 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.
• D. Resume with full history and configure the agent to automatically re-call all previously-used tools at session start to ensure data freshness.