Effective Work Samples for Evaluating Autonomous AI Agent Design Skills

Autonomous AI agents represent one of the most exciting frontiers in artificial intelligence, combining language models, tool use, memory systems, and planning capabilities to create systems that can operate independently to achieve complex goals. As organizations increasingly adopt these technologies, finding candidates with the right skills to design and implement effective autonomous agents has become critical to staying competitive.

Evaluating candidates for autonomous AI agent design roles presents unique challenges. Traditional interviews often fail to reveal a candidate's true capabilities in this rapidly evolving field. Technical knowledge alone isn't sufficient—successful agent designers must demonstrate systems thinking, creative problem-solving, and an understanding of how different components interact to create effective autonomous systems.

Work samples provide a window into how candidates approach real-world agent design challenges. By observing candidates as they plan, implement, debug, and refine autonomous agents, hiring teams can assess both technical proficiency and critical thinking skills. These exercises reveal how candidates balance competing priorities, handle ambiguity, and make design decisions that impact agent performance.

The following work samples are designed to evaluate key competencies for autonomous AI agent design roles. Each exercise targets specific skills while providing candidates the opportunity to demonstrate their unique approach to agent architecture and implementation. By incorporating these exercises into your interview process, you'll gain deeper insights into candidates' capabilities than traditional question-and-answer formats could provide.

Activity #1: Agent Architecture Design Challenge

This exercise evaluates a candidate's ability to design a comprehensive autonomous agent architecture. It reveals their understanding of agent components, their approach to system design, and their ability to make thoughtful architectural decisions. The exercise demonstrates how candidates think about the integration of various AI capabilities into a cohesive system.

Directions for the Company:

• Provide candidates with a detailed problem statement describing a business use case requiring an autonomous agent (e.g., a research assistant agent that can gather information, summarize findings, and generate reports).
• Include specific requirements the agent must fulfill and constraints it must operate within.
• Allow candidates 45-60 minutes to complete their design.
• Prepare a whiteboard or digital drawing tool for the candidate to sketch their architecture.
• Have a technical interviewer familiar with agent design principles conduct this exercise.

Directions for the Candidate:

• Design a comprehensive architecture for an autonomous agent that addresses the provided use case.
• Create a diagram showing the key components of your agent (e.g., memory systems, planning modules, tool integrations, etc.).
• Explain how information flows through your system and how different components interact.
• Identify potential failure points and how your design addresses them.
• Be prepared to explain your design choices and trade-offs you considered.

Feedback Mechanism:

• The interviewer should provide feedback on one strength of the architecture (e.g., "I like how you incorporated a verification step before taking actions") and one area for improvement (e.g., "The memory system might struggle with long-term context").
• After receiving feedback, give the candidate 10 minutes to revise their design, focusing specifically on the improvement area.
• Observe how receptive the candidate is to feedback and how effectively they incorporate it into their revised design.

Activity #2: Agent Implementation Exercise

This hands-on coding exercise evaluates a candidate's ability to implement a simple autonomous agent using modern frameworks and tools. It demonstrates their programming skills, familiarity with agent development libraries, and ability to translate design concepts into working code.

Directions for the Company:

• Prepare a development environment with necessary libraries pre-installed (e.g., LangChain, AutoGPT, or similar frameworks).
• Create a GitHub repository with starter code that includes basic imports and structure.
• Provide access to necessary API keys (using environment variables) for LLM access.
• Allow 60-90 minutes for this exercise.
• Consider making this a take-home assignment if time constraints are an issue.

Directions for the Candidate:

• Implement a simple autonomous agent that can perform a specific task (e.g., searching for information on a topic and creating a summary).
• Use the provided framework to implement the core agent functionality.
• Your implementation should include:
• A clear definition of the agent's goals
• Tool selection and integration
• Basic memory/context management
• A mechanism for the agent to determine when its task is complete
• Write clean, well-documented code that another developer could understand and maintain.
• Be prepared to explain your implementation choices.

Feedback Mechanism:

• The interviewer should provide specific feedback on one strength (e.g., "Your tool integration approach is very clean and extensible") and one area for improvement (e.g., "The agent might benefit from better error handling when API calls fail").
• Give the candidate 15 minutes to implement improvements based on the feedback.
• Assess both the quality of the initial implementation and how effectively the candidate incorporates feedback.

Activity #3: Agent Debugging and Optimization

This exercise tests a candidate's ability to analyze, debug, and improve an existing autonomous agent implementation. It reveals their problem-solving skills, attention to detail, and ability to optimize agent performance—critical skills for working with complex AI systems.

Directions for the Company:

• Prepare a flawed but functional autonomous agent implementation with several issues:
• Inefficient prompt design leading to high token usage
• Circular reasoning patterns where the agent gets stuck in loops
• Poor tool selection logic
• Memory leaks or context management issues
• Provide documentation explaining the agent's intended behavior.
• Include sample outputs showing the problematic behavior.
• Allow 60 minutes for this exercise.

Directions for the Candidate:

• Review the provided autonomous agent implementation and identify issues affecting its performance.
• Prioritize the problems based on their impact on agent functionality.
• Implement fixes for the highest-priority issues.
• Document the problems you found and your solutions.
• Suggest additional improvements that could be made with more time.
• Be prepared to explain your debugging process and reasoning behind your fixes.

Feedback Mechanism:

• The interviewer should acknowledge one effective fix the candidate implemented and suggest one alternative approach to a problem they addressed.
• Give the candidate 15 minutes to implement the alternative approach or explain why their original solution might be preferable in certain circumstances.
• Evaluate the candidate's systematic approach to problem identification and their ability to implement effective solutions.

Activity #4: Prompt Engineering for Autonomous Agents

This exercise evaluates a candidate's ability to design effective prompts and instructions for autonomous agents. It demonstrates their understanding of LLM capabilities and limitations, as well as their ability to craft clear, unambiguous instructions that guide agent behavior.

Directions for the Company:

• Prepare a scenario where an autonomous agent needs to perform a complex task requiring multiple steps and careful instruction following.
• Provide examples of poorly designed prompts that lead to suboptimal agent behavior.
• Include access to an LLM API for testing prompt effectiveness.
• Allow 45 minutes for this exercise.
• Prepare evaluation criteria for assessing prompt quality.

Directions for the Candidate:

• Design a system prompt and task-specific instructions for an autonomous agent that needs to perform the described task.
• Your prompt design should:
• Clearly define the agent's role and capabilities
• Establish constraints and guardrails
• Provide a clear reasoning framework
• Include mechanisms to prevent common failure modes (hallucination, prompt injection, etc.)
• Test your prompts using the provided LLM API and refine based on the results.
• Document your prompt design process and the iterations you went through.
• Be prepared to explain how your prompt design addresses potential failure modes.

Feedback Mechanism:

• The interviewer should highlight one effective element of the candidate's prompt design and suggest one area where the instructions could be more precise or effective.
• Give the candidate 10 minutes to revise their prompts based on the feedback.
• Evaluate both the initial prompt design and the candidate's ability to iterate and improve based on feedback.

Frequently Asked Questions

• How long should we allocate for these work samples?
  Each exercise is designed to take 45-90 minutes. For on-site interviews, you might select 1-2 exercises that best align with your priorities. Alternatively, consider making the implementation or debugging exercise a take-home assignment to allow candidates more time.
• What if candidates aren't familiar with the specific frameworks we use?
  Focus on evaluating the candidate's approach and reasoning rather than specific framework knowledge. Good autonomous agent designers can adapt to different tools. Consider providing a brief overview of your preferred frameworks before the exercise begins.
• How should we evaluate candidates who take different approaches to these exercises?
  There are many valid approaches to autonomous agent design. Evaluate candidates on the soundness of their reasoning, the clarity of their communication, and how well their solution addresses the core requirements—not on whether they match a predetermined "correct" answer.
• Should we provide access to external resources during these exercises?
  Yes, allow candidates to reference documentation and resources they would normally use in their work. This creates a more realistic environment and focuses evaluation on problem-solving rather than memorization.
• How can we adapt these exercises for candidates with different experience levels?
  For more junior candidates, provide additional structure and guidance. For senior candidates, introduce more complexity or constraints. The core exercises remain valuable across experience levels, but expectations should be calibrated appropriately.
• What if a candidate struggles with the initial implementation but excels at debugging?
  Different candidates have different strengths. Consider the full profile of skills demonstrated across exercises and how they align with your specific needs. Strong debugging skills might be more valuable than implementation speed for certain roles.

Implementing these work samples will significantly improve your ability to identify candidates with the skills needed to design effective autonomous AI agents. By observing candidates as they tackle realistic challenges, you'll gain insights into their technical abilities, problem-solving approaches, and communication skills that traditional interviews simply can't reveal.

For more resources to enhance your hiring process, explore Yardstick's suite of AI-powered tools, including our AI Job Description Generator, AI Interview Question Generator, and AI Interview Guide Generator. These tools can help you create comprehensive interview processes tailored to your specific needs.

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