Essential Work Sample Exercises for Evaluating LLM Agent Architecture and Orchestration Skills

LLM Agent Architecture and Orchestration has emerged as a critical skill set in the AI engineering landscape. As organizations increasingly deploy sophisticated AI systems, the ability to design, implement, and orchestrate LLM-based agents has become a valuable technical competency. These systems require engineers who understand not just the theoretical foundations of language models, but also how to architect robust agent systems that can work independently or in concert to solve complex problems.

Evaluating candidates for roles requiring LLM agent expertise presents unique challenges. Traditional interviews often fail to reveal a candidate's true capabilities in designing agent architectures, implementing prompt engineering strategies, debugging complex agent behaviors, or orchestrating multi-agent systems. Technical discussions alone may not demonstrate whether a candidate can translate theoretical knowledge into practical, working solutions.

Work sample exercises provide a window into how candidates approach real-world LLM agent challenges. By observing candidates as they design architectures, implement agent functionality, troubleshoot issues, and orchestrate complex systems, hiring teams can gain invaluable insights into their problem-solving processes, technical depth, and practical skills. These exercises reveal not just what candidates know, but how they apply that knowledge in realistic scenarios.

The following work samples are designed to evaluate different facets of LLM agent expertise, from high-level architecture design to hands-on implementation and debugging. Each exercise simulates challenges that professionals in this field regularly encounter, providing a comprehensive assessment of a candidate's capabilities. By incorporating these exercises into your interview process, you'll be better equipped to identify candidates who can truly excel in roles requiring LLM agent architecture and orchestration skills.

Activity #1: Agent Architecture Design Challenge

This exercise evaluates a candidate's ability to design a comprehensive LLM agent architecture for a specific use case. It tests their understanding of agent components, prompt engineering, memory systems, tool integration, and architectural considerations. Strong candidates will demonstrate thoughtful design choices that balance technical requirements with practical implementation concerns.

Directions for the Company:

• Prepare a detailed use case description for an LLM agent system. For example: "Design an agent system that helps customer service representatives by retrieving relevant information from knowledge bases, suggesting responses, and automating routine tasks."
• Provide constraints and requirements, such as: must handle multiple concurrent users, integrate with existing CRM systems, maintain conversation context across sessions, etc.
• Prepare a whiteboard or digital drawing tool for the candidate to sketch their architecture.
• Allocate 30-45 minutes for this exercise.
• Have a technical evaluator familiar with LLM agent architectures present to ask follow-up questions.

Directions for the Candidate:

• Review the use case and requirements carefully.
• Design a comprehensive LLM agent architecture that addresses the requirements.
• Create a diagram showing the key components of your architecture, including:
• LLM selection and integration
• Prompt engineering strategy
• Memory and context management
• Tool/API integrations
• Orchestration approach
• Error handling and fallback mechanisms
• Be prepared to explain your design choices and trade-offs.
• Consider both technical feasibility and practical implementation concerns.

Feedback Mechanism:

• After the candidate presents their architecture, provide feedback on one strength (e.g., "Your approach to context management is particularly robust") and one area for improvement (e.g., "The error handling mechanism might not scale well with multiple concurrent users").
• Ask the candidate to revise the specific portion of their architecture that could be improved, giving them 5-10 minutes to incorporate the feedback.
• Observe how receptive they are to feedback and how effectively they can adapt their design.

Activity #2: Implementing a Tool-Using Agent

This exercise tests a candidate's hands-on ability to implement a functional LLM agent that can use external tools. It evaluates coding skills, prompt engineering, tool integration, and practical implementation knowledge. This activity reveals whether candidates can translate theoretical understanding into working code.

Directions for the Company:

• Set up a development environment with necessary libraries (e.g., LangChain, OpenAI API, etc.) or allow candidates to use their preferred setup.
• Prepare a simple API or tool that the agent will need to use (e.g., a weather API, calculator function, or database query tool).
• Provide documentation for the API/tool and clear requirements for what the agent should accomplish.
• Allocate 60-90 minutes for this exercise.
• Prepare test cases to evaluate the agent's functionality.

Directions for the Candidate:

• Implement a functional LLM agent that can:
• Understand user requests related to the provided tool
• Determine when to use the tool based on user input
• Properly format requests to the tool
• Process tool responses and incorporate them into the agent's replies
• Handle basic error cases
• Write clean, well-documented code that demonstrates your implementation approach.
• Implement effective prompt engineering to guide the LLM's behavior.
• Be prepared to explain your implementation choices and demonstrate your agent working.
• Focus on creating a minimal viable implementation within the time constraints rather than a perfect solution.

Feedback Mechanism:

• Test the candidate's agent with prepared test cases and provide feedback on one strength (e.g., "Your prompt engineering effectively constrains the model's responses") and one area for improvement (e.g., "The agent doesn't handle API errors gracefully").
• Give the candidate 15 minutes to implement improvements based on the feedback.
• Observe how they prioritize improvements and implement changes under time pressure.

Activity #3: Agent Debugging and Optimization Challenge

This exercise evaluates a candidate's ability to troubleshoot, debug, and optimize an existing LLM agent system. It tests analytical thinking, problem diagnosis, and optimization skills. This activity reveals how candidates approach maintaining and improving complex systems they didn't originally build.

Directions for the Company:

• Prepare a functional but flawed LLM agent implementation with several issues:
• A prompt engineering issue causing occasional hallucinations
• An inefficient memory management approach
• A bug in tool usage logic
• Performance bottlenecks
• Provide documentation explaining the intended functionality and system architecture.
• Include logs or examples showing the problematic behaviors.
• Allocate 45-60 minutes for this exercise.

Directions for the Candidate:

• Review the provided agent implementation and documentation.
• Identify and document at least three issues affecting the agent's performance, reliability, or functionality.
• For each issue:
• Describe the problem and its impact
• Explain the root cause
• Propose a specific solution
• Implement fixes for at least one of the identified issues.
• Suggest optimization strategies that could improve the agent's performance or reduce costs.
• Be prepared to explain your diagnostic process and reasoning behind your solutions.

Feedback Mechanism:

• After the candidate presents their findings and solutions, provide feedback on one strength (e.g., "Your diagnosis of the memory management issue was spot-on") and one area for improvement (e.g., "Your solution might introduce new edge cases").
• Ask the candidate to refine their approach to the area needing improvement, giving them 10 minutes to develop a more robust solution.
• Discuss how they would validate that their solutions actually fixed the problems.

Activity #4: Multi-Agent Orchestration Design

This exercise tests a candidate's ability to design and implement orchestration for a multi-agent system. It evaluates understanding of agent collaboration, task routing, and complex system design. This activity reveals whether candidates can architect sophisticated systems where multiple agents work together effectively.

Directions for the Company:

• Prepare a scenario requiring multiple specialized agents working together, such as a research assistant system with separate agents for search, summarization, fact-checking, and user interaction.
• Define the capabilities and limitations of each agent in the system.
• Provide clear requirements for how the agents should collaborate.
• Prepare a coding environment with basic agent implementations or skeletons.
• Allocate 60-90 minutes for this exercise.

Directions for the Candidate:

• Design an orchestration system that enables effective collaboration between the provided agents.
• Your orchestration system should:
• Route tasks to appropriate agents based on their specializations
• Manage information flow between agents
• Handle dependencies between agent tasks
• Maintain overall system coherence
• Implement error handling and recovery mechanisms
• Implement a proof-of-concept of your orchestration system, focusing on the core logic rather than perfecting every detail.
• Document your design decisions and the architecture of your orchestration system.
• Be prepared to demonstrate your system with a simple end-to-end example.

Feedback Mechanism:

• After the candidate presents their orchestration system, provide feedback on one strength (e.g., "Your task routing logic is elegant and extensible") and one area for improvement (e.g., "The system doesn't handle partial agent failures gracefully").
• Ask the candidate to enhance the specific aspect of their system that needs improvement, giving them 15 minutes to implement changes.
• Discuss how their orchestration approach would scale if more agents were added to the system.

Frequently Asked Questions

How should we adapt these exercises for candidates with different experience levels?

For junior candidates, consider simplifying the requirements and providing more scaffolding. For example, in the implementation exercise, you might provide starter code or a more detailed framework. For senior candidates, add complexity such as scalability requirements or more nuanced edge cases to handle.

What if we don't have the technical expertise to evaluate these exercises in-house?

Consider bringing in a technical consultant or advisor specifically for these interviews. Alternatively, focus on the design exercises rather than implementation, as they're often easier to evaluate based on the candidate's explanation of their approach and reasoning.

How can we make these exercises fair for candidates who may be familiar with different LLM frameworks or tools?

Allow candidates to use their preferred frameworks and tools when possible. Focus your evaluation on their understanding of core concepts and problem-solving approach rather than specific implementation details. Make sure to communicate available options in advance so candidates can prepare accordingly.

Should candidates be allowed to use reference materials or look things up during these exercises?

Yes, in most real-world scenarios, engineers have access to documentation and resources. Allowing reference materials creates a more realistic environment and reduces unnecessary stress. However, candidates should still demonstrate fundamental understanding of key concepts without having to look up basics.

How can we ensure these exercises don't take too much of the candidate's time?

Be clear about time expectations upfront. Consider offering these exercises as take-home assignments with reasonable time limits, or schedule dedicated technical interview sessions. For on-site exercises, carefully scope the requirements to be challenging but achievable within the allocated time.

What if a candidate proposes a valid approach that's different from what we expected?

This is actually valuable information! Different approaches can highlight creative thinking and diverse experience. Evaluate based on whether their solution effectively addresses the requirements, not whether it matches a predetermined "correct" answer. Use these moments to learn from candidates who might bring fresh perspectives.

LLM Agent Architecture and Orchestration is a rapidly evolving field, and finding candidates with the right mix of theoretical knowledge and practical skills is challenging. These work sample exercises provide a structured way to evaluate candidates' capabilities across the spectrum of skills needed for success in this domain. By incorporating these exercises into your hiring process, you'll be better equipped to identify candidates who can design, implement, and orchestrate effective LLM agent systems for your organization.

For more resources to improve your hiring process, check out Yardstick's AI Job Description Generator, AI Interview Question Generator, and AI Interview Guide Generator. These tools can help you create comprehensive job descriptions, develop targeted interview questions, and design structured interview guides that complement these work sample exercises.

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