You're managing an agentic AI responsible for customer support ticket triage. The agent has been consistently accurate in routing tickets to the appropriate departments. However, a team leader has noticed a significant increase in the number of tickets requiring "escalation" - cases where the agent initially misclassified a complex issue as a simple, routine one, leading to delays and frustrated customers.
What would be an appropriate first step in resolving this issue?

• A. Conducting a "red-teaming" exercise, having human agents deliberately create complex and ambiguous scenarios to analyze the agent's robustness.
• B. Analyzing the agent's decision-making process, focusing on the specific criteria it uses to classify tickets, and identifying potential biases or blind spots.
• C. Adjusting the agent's reward function to prioritize speed of resolution over accuracy, as a first step in analysis of the problem.
• D. Increasing the agent's autonomy, granting it more decision-making power during triage to improve its efficiency.

An agentic AI is tasked with generating marketing copy for various campaigns. It's consistently producing high-quality text and generating significant engagement. However, qualitative feedback from brand managers indicates that the content lacks a distinct "brand voice" and feels generic.
Which of the following metrics would be most valuable for evaluating the agent's adherence to the brand's established voice?

• A. A metric quantifying how frequently the agent's output is shared, liked, or reposted on major social platforms, using this as an indicator of effective brand representation.
• B. A metric assessing the agent's ability to tailor its language and messaging for distinct audience segments based on demographic and psychographic data.
• C. A metric tracking the average word count and sentence length of the agent's copy, focusing on stylistic efficiency as a potential proxy for brand alignment.
• D. A metric evaluating the agent's textual similarity to a formalized brand style guide, analyzing factors such as tone, approved vocabulary, and prescribed sentence structures.

When analyzing suboptimal agent response quality after deployment, which parameter tuning evaluation methods effectively identify the optimal configuration adjustments? (Choose two.)

• A. Apply identical parameter settings across all agent types and tasks, promoting consistency and simplifying comparison across different use cases.
• B. Implement A/B testing frameworks comparing temperature, top-k, and top-p variations while measuring task-specific quality metrics and user satisfaction scores.
• C. Design ablation studies systematically varying individual parameters while holding others constant to isolate each parameter's impact on agent behavior and performance.
• D. Use production traffic directly for parameter experiments, enabling real-world insights and faster identification of impactful settings.
• E. Randomly adjust all parameters simultaneously, allowing for broader exploration of the parameter space in a shorter time frame.

When implementing tool orchestration for an agent that needs to dynamically select from multiple tools (calculator, web search, API calls), which selection strategy provides the most reliable results?

• A. LLM-based tool selection with structured tool descriptions and usage examples
• B. Rule-based selection with predefined tool mappings and usage examples
• C. Configuration-based tool selection with manual specifications and usage examples
• D. Random dynamic tool selection with retry mechanisms and usage examples

What is RAG Fusion primarily designed to achieve?

• A. Creating a separate, dedicated database for storing all the retrieved chunks.
• B. Blending information from multiple retrieved chunks into a single response generated by the LLM.
• C. Automatically translating and integrating all retrieved chunks into a single language.
• D. Minimizing the need for retrieval, allowing the LLM to generate responses directly from its internal knowledge.