AI Tools: Evaluate a New Coding Agent Without Marketing Bias

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-tools-evaluate-a-coding-agent-r8a1-creators

1. A developer wants to try a new AI coding agent. The vendor shows a demo where the agent fixes a perfectly organized, well-documented codebase in 30 seconds. Why is this demo unreliable for making an adoption decision?

   1. The vendor is intentionally lying about the agent's capabilities
   2. The demo is too fast and doesn't give the agent enough time to work
   3. The demo uses ideal conditions that don't reflect real-world complexity and messiness
   4. The agent only works on documentation, not actual code

2. When structuring an evaluation of a new coding agent, how many representative tasks should you select from your backlog?

   1. As many tasks as possible to be thorough
   2. Only the most difficult tasks to test the agent's limits
   3. 3-5 tasks covering different types of work
   4. One task to get a quick sense of the tool

3. A developer runs an evaluation of Agent X on three bugs from their backlog, giving each bug 15 minutes. What important evaluation principle is being applied?

   1. Giving the agent unlimited time to ensure quality
   2. Testing the agent's ability to work under pressure
   3. Time-boxing each task to simulate real work conditions
   4. Comparing the agent against other agents on the same bugs

4. When scoring an AI coding agent during evaluation, which THREE metrics should you assess? (Select the three that the lesson recommends.)

   1. User interface design, color scheme, and ease of installation
   2. Speed, correctness, and follow-up time
   3. Popularity, cost, and marketing claims
   4. Code style, variable names, and comment quality

5. A developer evaluates a new coding agent and finds it completes tasks 40% faster than their current tool. They immediately decide to switch. Why might this be premature?

   1. The agent may regress after the novelty period ends
   2. The developer should switch anyway for any speed improvement
   3. Speed is not a meaningful metric for coding agents
   4. Current tools cannot be compared fairly to new agents

6. What does the lesson identify as a key limitation that cannot be determined from a 90-minute evaluation?

   1. How many files the agent can process at once
   2. Whether the agent supports Python language
   3. How the agent will affect team productivity over 6 months
   4. Whether the agent can write comments in code

7. What is the 'novelty bias' phenomenon in the context of adopting new AI coding tools?

   1. The tendency for developers to forget how to use old tools
   2. The tendency for new tools to have more bugs initially
   3. The tendency for old tools to resist new features
   4. The tendency for new tools to seem better because they are new and exciting

8. When should you run a second evaluation of a coding agent before making a final adoption decision?

   1. After reading the vendor's documentation
   2. Only when the vendor releases an update
   3. Immediately after the first evaluation
   4. At week 4 after regular use

9. In the context of this lesson, what is a 'pilot' when adopting a new coding agent?

   1. The vendor's demonstration of the product
   2. A small test deployment to evaluate the tool in real work before full commitment
   3. A competition between two different coding agents
   4. A document describing the tool's features

10. A developer creates a scoring system with categories like 'code compiles on first try,' 'handles edge cases,' and 'produces readable output.' What is this scoring system called in the lesson?

    1. A rubric
    2. A vendor scorecard
    3. A benchmark
    4. A marketing claim

11. Why is comparing a new coding agent against your existing tool on the SAME tasks important?

    1. The vendor requires this comparison
    2. The existing tool is always worse than new tools
    3. It makes the evaluation take longer
    4. It provides a fair baseline for measuring improvement

12. What does the lesson say about using 'ideal repos' for evaluating coding agents?

    1. They are better than using your own codebase
    2. They don't reflect real-world conditions and lead to unreliable evaluations
    3. They should be used for initial testing only
    4. They provide the most accurate performance prediction

13. What is 'follow-up time' as a scoring metric for coding agents?

    1. How quickly the vendor responds to support requests
    2. How long you wait for the agent to generate code
    3. How much time you spend fixing or adjusting the agent's output
    4. How long the agent takes to start up

14. The lesson mentions that a 90-minute evaluation cannot account for which of the following?

    1. The color scheme of the agent's interface
    2. The team's learning curve when adopting the tool
    3. How fast the agent generates code
    4. Whether the agent supports multiple programming languages

15. What makes a task 'representative' when selecting tasks for agent evaluation?

    1. It is a task you would normally do in your regular work
    2. It is a task the agent was specifically designed for
    3. It is the most difficult task in your backlog
    4. It is a task the vendor recommends