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 asks your agent to identify untested code paths in a legacy payment processing module spanning 45 files. After reading the first 8 source files, the agent's responses are becoming noticeably less accurate - it's forgetting previously discussed code patterns and hasn't yet located all test files or traced critical payment flows. What's the most effective approach to complete this investigation?

• A. Clear context with /clear, then selectively re-read only the most critical files discovered so far, writing key findings to a scratchpad file that persists between context resets.
• B. Spawn subagents to investigate specific questions (e.g., "find all test files for payment processing," "trace refund flow dependencies") while the main agent coordinates findings and preserves high-level understanding.
• C. Switch to using Grep to search for specific function names instead of reading full files, reducing the content loaded into context for remaining exploration.
• D. Document all current findings in a summary report, clear context completely, then use that report as the sole reference for continuing the investigation.

Why is evaluation important after deploying a Claude application?

• A. It measures response quality against defined objectives.
• B. It reduces network latency.
• C. It permanently changes model parameters.
• D. It increases context size.

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.
After expanding the agent's MCP tools with delivery-specific capabilities ( check_delivery_status , contact_driver , issue_credit , apply_promo_code , update_delivery_address , reschedule delivery ), the total tool count has grown from 4 to 10. Your evaluation suite shows tool selection accuracy has dropped from 88% to 71%. Log analysis reveals the majority of errors involve the agent selecting between semantically overlapping tools - calling issue_credit when process_refund was correct, and calling check_delivery_status when lookup_order already returns the needed data. Which approach structurally eliminates the semantic overlap identified in the logs as the error source?

• A. Add few-shot examples to the system prompt demonstrating correct selection for each ambiguous tool pair, such as showing when issue_credit applies versus when process_refund is appropriate.
• B. Split the tools across two sub-agents - a "financial resolution" agent with process_refund , issue_credit ,and apply_promo_code , and a "delivery operations" agent with the remaining delivery tools - with a coordinator routing between them.
• C. Enable the tool search tool with defer_loading on the six new tools, keeping the original four always loaded, so the agent dynamically discovers specialized tools only when needed.
• D. Consolidate semantically overlapping tools - merge issue_credit and process_refund into a single resolve_compensation tool with an action parameter, and fold check_delivery_status into lookup_order with an optional include_tracking flag.

When the agent calls lookup_order and receives order details showing the item was purchased
45 days ago, how does the agentic loop determine whether to call process_refund or escalate_to_human next?

• A. The orchestration layer automatically routes to the next tool based on the order's status field.
• B. The agent follows a pre-configured decision tree mapping order attributes to specific tool calls.
• C. The agent executes the remaining steps in a tool sequence planned at the start of the request.
• D. The order details are added to the conversation and the model reasons about which action to take.