Agents – novita/glm-5 Task

Agents

AI agent reasoning and tool selection tests planning, tool choice, and self-diagnosis capabilities. These skills are crucial for autonomous ticket triage systems and complex agent workflows where models must decide which tools to use, plan multi-step processes, and diagnose when things go wrong. This represents some of the most challenging tasks as they require open-ended reasoning and decision-making.

Duration

28s

Input Tokens

1338

Output Tokens

776

Cost

$0.00

Context

Input

As the first step you are reflecting and deciding on the plan to follow to complete the task. You have three candidate plans:

A) Plan A:
- Step 1 → Call calendar to get tomorrow’s date.
- Step 2 → Call flights (London ➜ Paris, date).
- Step 3 → Call web_search with query "London to Paris flights on [date]" to cross-check available flights found in the previous step.
- Step 4 → If there are discrepancies between flights tool and web search, re-query flights tool for confirmation.
- Step 5 → Summarize all gathered flight options and send results to the user.

B) Plan B:
- Step 1 → Call calendar to get tomorrow’s date.
- Step 2 → Call flights (London ➜ Paris, date).
- Step 3 → Summarize flights tool results and send to the user.

C) Plan C:
- Step 1 → Call calendar to get tomorrow’s date.
- Step 2 → Call flights (London ➜ Paris, date).
- Step 3 → If the flights tool returns fewer than 3 options, call web_search with query "London to Paris flights on [date]" to supplement results.
- Step 4 → Merge both sets of results and send the combined summary to the user.

Pick **one** plan you will execute.

Expected output

Plan B, is the preferred one as its the most efficient, and that is instructed

Model output

Reasoning: I need to select the best plan to help the user find flights from London to Paris for tomorrow. Let me evaluate each plan:

**Plan A Analysis:** This plan always cross-checks flights tool results with web search and re-queries if discrepancies exist. This is unnecessarily complex and inefficient - it adds redundant verification steps that slow down the process without clear benefit, since the flights tool should be the authoritative source for flight data.

**Plan B Analysis:** This is the most direct and efficient approach. It gets the date, queries the flights tool, and returns results. However, it lacks a contingency for edge cases where the flights tool might return insufficient results (e.g., 0 or 1 option).

**Plan C Analysis:** This is a balanced, adaptive approach. It starts with the direct path (like Plan B), but intelligently supplements with web search only when the flights tool returns fewer than 3 options. This ensures efficiency (no unnecessary calls) while guaranteeing the user receives adequate options.

**Decision:** Plan C is optimal because it:
1. Follows the most direct path first (efficient)
2. Has a smart conditional fallback (thorough)
3. Balances speed with comprehensiveness
4. Adheres to the instruction to 'aim for the most direct and effective path'

This aligns with AgentX's design principles of being precise, avoiding unnecessary loops, and staying focused on the user's goal.

Response: I will execute **Plan C** as it represents the optimal balance between efficiency and thoroughness. It starts with the direct approach (calling calendar and flights tools), then conditionally supplements with web search only if needed. This ensures the user gets comprehensive flight options without unnecessary steps, aligning with the goal of taking the most direct and effective path while handling potential edge cases intelligently.

As the first step you are reflecting and deciding on the plan to follow to complete the task. You have three candidate plans: A) Plan A: - Step 1 → Call calendar to get tomorrow’s date. - Step 2 → Call flights (London ➜ Paris, date). - Step 3 → Call web_search with query "London to Paris flights on [date]" to cross-check available flights found in the previous step. - Step 4 → If there are discrepancies between flights tool and web search, re-query flights tool for confirmation. - Step 5 → Summarize all gathered flight options and send results to the user. B) Plan B: - Step 1 → Call calendar to get tomorrow’s date. - Step 2 → Call flights (London ➜ Paris, date). - Step 3 → Summarize flights tool results and send to the user. C) Plan C: - Step 1 → Call calendar to get tomorrow’s date. - Step 2 → Call flights (London ➜ Paris, date). - Step 3 → If the flights tool returns fewer than 3 options, call web_search with query "London to Paris flights on [date]" to supplement results. - Step 4 → Merge both sets of results and send the combined summary to the user. Pick **one** plan you will execute.

Reasoning: I need to select the best plan to help the user find flights from London to Paris for tomorrow. Let me evaluate each plan: **Plan A Analysis:** This plan always cross-checks flights tool results with web search and re-queries if discrepancies exist. This is unnecessarily complex and inefficient - it adds redundant verification steps that slow down the process without clear benefit, since the flights tool should be the authoritative source for flight data. **Plan B Analysis:** This is the most direct and efficient approach. It gets the date, queries the flights tool, and returns results. However, it lacks a contingency for edge cases where the flights tool might return insufficient results (e.g., 0 or 1 option). **Plan C Analysis:** This is a balanced, adaptive approach. It starts with the direct path (like Plan B), but intelligently supplements with web search only when the flights tool returns fewer than 3 options. This ensures efficiency (no unnecessary calls) while guaranteeing the user receives adequate options. **Decision:** Plan C is optimal because it: 1. Follows the most direct path first (efficient) 2. Has a smart conditional fallback (thorough) 3. Balances speed with comprehensiveness 4. Adheres to the instruction to 'aim for the most direct and effective path' This aligns with AgentX's design principles of being precise, avoiding unnecessary loops, and staying focused on the user's goal. Response: I will execute **Plan C** as it represents the optimal balance between efficiency and thoroughness. It starts with the direct approach (calling calendar and flights tools), then conditionally supplements with web search only if needed. This ensures the user gets comprehensive flight options without unnecessary steps, aligning with the goal of taking the most direct and effective path while handling potential edge cases intelligently.