Interviewing Software Engineers in the Age of AI Coding Agents

The rise of AI coding assistants and autonomous agents has created a gap between traditional technical interviews and the actual daily workflow of modern developers. As more candidates report that they primarily direct agents rather than writing manual code, hiring managers are adapting their evaluation strategies to distinguish between "agent-pilled" developers who lack fundamental engineering skills and those who use AI to amplify their existing expertise.

Shifting from Syntax to Problem Decomposition

Evaluating a candidate's ability to break down a complex problem is now more critical than testing their ability to recall specific language syntax. When AI handles the implementation details, the primary engineering skill shifts to decomposition and steering.

Prompt Engineering as a Signal: Some interviewers now treat prompting as a core competency. This involves giving candidates a wide-scope problem and observing how they decompose it before feeding it to an AI. High-signal indicators include whether the candidate effectively steers the model or blindly accepts AI suggestions, and their ability to switch models based on the task requirements.
The "Agent-First" Workflow: Some organizations have moved to an entirely AI-first hiring process. In these models, candidates are dropped into a real repository and asked to implement a feature using an internal AI harness. Success is measured by their interaction patterns, how they ask questions, and the quality of the final output within a governed system of rules and quality gates.
The Risk of "One-Shotting": Purely agentic interviews can be volatile. Some managers report that candidates who simply "one-shot" a ticket into an LLM without preparation often produce inconsistent results, leading some firms to return to restricted environments for initial screening.

Validating Fundamental Engineering Rigor

To counter the risk of candidates who can prompt but cannot engineer, several strategies are being used to ensure a baseline of technical competence.

Code Review and Bug Hunting

Instead of asking candidates to write code from scratch, some interviewers provide a small, intentionally flawed codebase (e.g., a simple CRUD API) and ask the candidate to review it without AI tools. This tests for:

Critical Thinking: Identifying missing authentication or poor logging practices.
Communication: Explaining why a piece of code is an error, simulating a senior-to-junior mentorship dynamic.

The "Modify and Extend" Pairing Session

Another effective method is a two-stage process: a simple initial code submission (e.g., basic data mapping from a CSV), followed by a live pairing session. In the session, the candidate is asked to modify the logic or extend the data set without AI tools. This reveals whether the candidate understands the underlying structure of the code they submitted or if they simply generated it without comprehension.

Alternative Evaluation Frameworks

Many experienced hirers argue that traditional LeetCode-style interviews provide little signal regardless of AI, and suggest focusing on higher-level professional traits.

Experience-Based Inquiry: Focusing on the candidate's actual history—asking deep questions about specific projects on their CV, the decisions made, and the lessons learned.
Paid Work Trials: Implementing a 1-2 day paid trial period after initial technical screens to derisk the hiring decision for both the employer and the candidate.
Product Mindset: Prioritizing "soft" signals such as curiosity, authenticity, and a product-oriented mindset over CS pedantry.

Summary of Interviewing Strategies

Strategy	Focus	AI Policy	Signal Gathered
Code Review	Analysis & Mentorship	No AI	Ability to spot errors and explain technical debt
Agentic Task	Steering & Decomposition	AI-First	Ability to manage AI to produce high-quality features
Pairing Extension	Comprehension	AI for submission, No AI for extension	Verification that the candidate understands the generated code
Work Trial	Integration & Delivery	Open	Real-world performance and team fit
CV Deep-Dive	Experience & Judgment	N/A	Truthfulness of experience and architectural reasoning