AI Agility (Developer) Assessment to evaluate generative AI readiness in software development
The Mercer AI Agility (Developer) Assessment is a structured assessment designed to evaluate developers’ ability to use generative AI tools in software development workflows. The assessment measures understanding of generative AI capabilities, prompt engineering techniques, and AI-assisted coding workflows used in development tasks. By focusing on development scenarios, the assessment evaluates how developers guide AI tools, validate generated outputs, and ensure alignment with engineering standards and system architecture. It enables organizations to identify developers who can integrate generative AI into development workflows while maintaining software quality and reliability.
About the Mercer AI Agility (Developer) Assessment
The Mercer AI Agility (Developer) Assessment evaluates developers’ readiness to use generative AI tools in software development workflows. As generative AI becomes integrated into coding, debugging, and documentation tasks, developers must understand how to apply these tools while ensuring that generated outputs align with established engineering standards.
This assessment measures the competencies required to use generative AI tools effectively during software development tasks. It evaluates how developers construct prompts, interpret model outputs, validate AI-generated code, and integrate these outputs into existing development pipelines while ensuring alignment with system architecture and development standards.
By providing objective insights into developers’ ability to use generative AI within development workflows, the assessment helps organizations identify professionals who can integrate these tools into software development while maintaining software quality and reliability.
What is inside this framework?
The AI Agility (Developer) Assessment evaluates developers across several competency areas that reflect the practical use of generative AI tools in software development workflows.
Business context and ROI awareness
This section measures developers’ ability to evaluate when generative AI should be used within development processes.
- Use case identification: Recognizing development scenarios where generative AI can improve productivity or output quality.
- Cost and token awareness: Designing AI-assisted workflows that remain efficient and scalable.
Advantages of the AI Agility (Developer) Assessment
The Mercer’s AI Agility (Developer) Assessment helps organizations introduce structure and consistency into evaluating developers’ readiness to work with generative AI tools. As AI-assisted development is becoming more common across software teams, organizations need reliable methods to determine whether developers can use these tools efficiently while maintaining engineering standards and code quality. This assessment provides an objective framework for identifying developers who can apply generative AI effectively in real development workflows.
- Standardized AI capability screening: Provides a consistent framework for evaluating developers’ ability to use generative AI tools across diverse candidate pools.
- Objective validation of AI-assisted development skills: Evaluates capabilities such as prompt engineering, code validation, and AI-assisted development workflows beyond self-reported experience.
- Improved hiring accuracy for AI-enabled development roles: Helps organizations identify developers who can apply generative AI within coding workflows while maintaining software quality and engineering discipline.
- More focused technical interviews: Allows interview stages to shift toward deeper technical discussions focusing on reasoning, design choices, and technical judgment.
- Supports scalable hiring and workforce development: Enables organizations to assess AI readiness consistently as engineering teams adopt AI-assisted development practices.
Use cases of the Mercer AI Agility (Developer) Assessment
Organizations can use the AI Agility (Developer) Assessment across multiple hiring and workforce development initiatives as generative AI tools become integrated into software engineering workflows.
- Hiring developers for AI-assisted software development roles: Identifies candidates who can use generative AI tools effectively within development workflows while maintaining engineering standards.
- Early technical screening: Helps recruiters evaluate candidates’ ability to use generative AI tools before progressing to deeper technical interviews.
- Developer training and upskilling initiatives: Highlights capability gaps in areas such as prompt engineering, AI-assisted coding, and output validation to support targeted training programs.
- Evaluating engineering team readiness for AI adoption: Enables technology leaders to assess how prepared development teams are to incorporate generative AI tools into development workflows.
- Supporting AI-driven workforce transformation: Provides structured insights that help organizations build AI-ready engineering teams as generative AI becomes part of modern development practices.
AI Agility (Developer) Assessment Competency Framework
Get a detailed look inside the test
AI Agility (Developer) Assessment competencies under scanner
LLM foundations and concepts
Prompt engineering and interaction
Code verification and quality
Security, hygiene, and compliance
Competencies:
Understanding how different generative AI models can be applied to specific development tasks.
Knowledge of model parameters that influence output generation and behavior.
Understanding how external knowledge sources can improve output accuracy and reduce hallucinations.
Competencies:
Designing prompts that provide sufficient context to generate accurate outputs and minimize hallucinations.
Refining prompts through multiple iterations to improve responses for complex development tasks.
Managing longer interactions with AI systems while maintaining efficiency and clarity.
Competencies:
Identifying structural issues or inefficient coding patterns in the generated outputs.
Ensuring that the generated code functions correctly and meets required quality standards.
Identifying hallucinated or misleading outputs generated by AI systems.
Verifying that the generated code aligns with the existing system architecture and development standards.
Competencies:
Preventing malicious inputs that manipulate AI-generated outputs.
Ensuring that sensitive information is not exposed through AI interactions.
Maintaining records of prompts and outputs to support traceability.
Customize This AI Agility (Developer) Assessment
Flexible customization options to suit your needs
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The Mercer | Mettl AI Agility (Developer) Assessment Advantage
- Industry Leading 24/7 Support
- State of the art examination platform
- Inbuilt Cutting Edge AI-Driven Proctoring
- Simulators designed by developers
- Tests Tailored to Your business needs
- Support for 20+ Languages in 80+ Countries Globally
Frequently Asked Questions (FAQs)
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The data obtained across industries and verticals of various types of organizations is updated in the Mercer Assessments database periodically. Utmost care is taken to ensure that the newly added data gets incorporated periodically while preparing the reports.
