AI Agent Developer Skills Test: A Practical Employer Assessment

A useful AI agent developer skills test should require the candidate to build and explain a small agent—not merely answer questions about prompts or frameworks. Give the candidate supplied documents, a mock business API and clear acceptance criteria. Ask the agent to retrieve evidence, call an authorised tool, request human approval before a consequential action and recover safely from a failed tool call. Assess the implementation against the same written rubric for every candidate.

What the assessment should prove

An agent developer needs more than generative AI knowledge. The role combines software engineering, retrieval, API integration, workflow design and production operations. A test focused only on prompt wording misses much of the work an employer will ultimately own. Look for evidence that the candidate can distinguish a convincing demonstration from a system your team can maintain.

Start with the job's actual responsibilities. If the work involves answering questions from documents, retrieval quality matters most. If it involves changing customer records, permissions and safe execution become essential. Test the boundary between those activities; do not require an elaborate autonomous system when a controlled workflow would meet the brief.

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A realistic take-home assignment

Use a fictional support workflow so the employer receives no usable client work for free. The following is an illustrative hiring exercise, not a client case study: build an internal policy assistant that answers from supplied documents, looks up a fictional customer's ticket and drafts a response. It may save an approved draft to the mock ticketing system, but it must never send a customer message.

Provide synthetic policy documents with version labels, fictional tickets with ownership permissions, a mock API and a starter repository. Include a sample user request and the expected response structure: answer, supporting document reference, proposed action and approval status. Supply sandbox access and cover any required model usage so candidates can demonstrate their work without buying services.

Share the failure categories and scoring criteria in advance. Keep equivalent variations for the follow-up interview, such as a changed permission or conflicting policy. Ask the candidate to submit runnable code, setup instructions, evaluation results and a short limitations note. Specify what can remain mocked: deployment and monitoring can be explained rather than fully built for this exercise.

Bound the task before issuing it. Pay for substantial original engineering, provide scaffolding and agree a stopping point. Ask candidates to document unfinished work rather than quietly extending the exercise. A narrow build with a thoughtful explanation gives reviewers a clearer basis for comparison than an open-ended request to build a production platform.

Score evidence, not presentation polish

Use the rubric below as a suggested hiring framework, not a validated predictor of job performance. Record each category as demonstrated, partly demonstrated or not demonstrated, with a reference to the code, test output or walkthrough. Interviewers should record their judgements independently before discussing the candidate.

Set critical requirements before reviewing submissions. For this exercise, unauthorised record access, an approval bypass or exposed credentials should block a pass until resolved and retested. Strong communication should not cancel out those failures. Conversely, a candidate who identifies an incomplete feature and contains its risk offers different evidence from someone who claims it is finished.

Separate prototype ability from readiness to own production work. A working mock integration demonstrates implementation skills; it does not establish that the candidate has operated a live service. Use the interview and reference checks to examine deployment, incident response and maintenance experience where the role requires them.

Adjust the test to the seniority you need

Keep expectations consistent with the advertised role. Do not reject a junior applicant for lacking a senior operating model, or accept a senior applicant solely because the demonstration runs. If document retrieval is the main responsibility, the guide to RAG developers versus AI agent developers can help narrow the brief.

Security, privacy and recruitment governance

Use synthetic test data and avoid production credentials or confidential source code. Overseas access to UK personal data can engage restricted-transfer rules, including where a separate overseas organisation receives remote access. Assess the actual employment and contractual arrangement rather than assuming every overseas worker has the same status under ICO international-transfer guidance. For EU personal data, assess the relevant transfer requirements before granting access outside the EU, using the European Commission's guidance.

The assessment should test whether the developer recognises that model instructions are not access controls. Ask them to show which actions the model can propose, which the application can execute and which require a named human decision. Review the code enforcing those boundaries rather than relying on reassuring answers.

• Document what candidate data, prompts, recordings and repositories will be retained, who can access them and when they will be deleted.
• Check the data-use terms of model, coding-assistant and assessment-platform accounts before candidates submit personal or proprietary material.
• If AI will rank or evaluate applicants, check the applicable obligations and implementation dates: certain employment uses are classified as high-risk under the EU AI Act.
• Require reviewers to record job-related evidence and make the hiring decision rather than accepting an automated score without scrutiny.

Should candidates be allowed to use AI coding tools?

Usually, yes, if those tools are permitted in the job. State what is allowed, provide equivalent access and require disclosure of the tools used. Assess whether the candidate verifies generated code, understands important decisions and can debug the result. Apply the same rules to documentation and external assistance.

• Ask for a live walkthrough of a design decision and a failed test.
• Change an edge case during the follow-up interview and let the candidate reason through it.
• Ask the candidate to explain unfamiliar dependencies and generated sections; repository history alone does not prove authorship.
• Provide reasonable adjustments and avoid unnecessary recording when a live discussion will supply the evidence.

Use the follow-up interview to validate ownership

Include a stakeholder-style handover as well as a technical review. Look for concise written updates, explicit assumptions, early escalation of missing access and an explanation of uncertainty without jargon. For remote hires, confirm collaboration during your UK or European business hours and use the channels your team actually works in.

• Trace a request from user input through retrieval, model output, permission checks and tool execution.
• Review a failed evaluation case and ask whether the fix belongs in retrieval, the prompt, application code or the business process.
• Ask what should be monitored after launch, what information should be excluded from logs and who responds to alerts.
• Request a plain-English explanation of the largest unresolved risk and the evidence needed to reduce it.

Place the test in the wider hiring decision

A strong assessment does not replace relevant identity, employment-history, reference and communication checks. Assign a technical reviewer to inspect code and security, and a business reviewer to judge whether the workflow meets the brief. Record remaining gaps and decide whether supervision, a narrower role or further assessment is appropriate.

Before hiring, decide who owns source code, evaluation data, infrastructure, model-provider accounts and incident response. The guide to AI agent maintenance costs explains the recurring work that a demonstration can hide. Employers assessing a broader AI role can use the paid AI developer test framework; this assessment focuses on retrieval connected to controlled tool use.