Hire the
Agent-Native
Engineer.
Traditional coding tests are obsolete. DevMesh evaluates how candidates orchestrate AI agents to solve complex, ambiguous problems.
The Assessment Gap
The technical hiring process is experiencing a legitimacy crisis. For decades, the industry has relied on a static model: data structures and algorithms (DSA) challenges, live unassisted coding, and system design.
The core assumption that candidates must code independently and from scratch is now obsolete. This model worked when developers wrote code line-by-line. But in 2025, that world no longer exists.
The Reality Check
- GitHub Copilot has 1.3M+ subscribers.
- 92% of developers use ChatGPT/Claude regularly.
- Speed is no longer about typing; it's about orchestration.
Market Signals
Meta's Pivot
A tier-1 tech giant publicly piloting AI-assisted interviews legitimizes the shift. Unassisted coding is now officially outdated.
Incumbent Stagnation
Platforms like HackerRank and LeetCode are trapped. They cannot embrace AI without invalidating their entire business model.
The IDE Revolution
Tools like Cursor and Windsurf have replaced the blank canvas. Developers now edit AI-generated streams rather than typing character-by-character.
The New Capabilities
When syntax is automated, engineering value shifts to higher-order thinking. We evaluate the skills that actually drive modern productivity.
Decomposition
The ability to break down ambiguous business requirements into precise technical specifications that AI agents can execute.
Critical Evaluation
Vigilance in reviewing AI-generated code for subtle logic bugs, security vulnerabilities, and edge cases that automated tools miss.
Integration
Stitching together disparate AI-generated components into a cohesive, scalable, and maintainable system architecture.
Architectural Judgment
Making high-stakes trade-off decisions (e.g., consistency vs. availability) that require context no LLM currently possesses.
The Industry is changing: Don't ban the tool; test the ability to wield it.
Solution: Multi-Agent Protocol
Candidates interact with specific personas, not a generic chatbot. The system acts as an orchestration layer, routing messages between specialized agents while maintaining a deterministic state machine.
Requirements Agent
Simulates non-technical stakeholders. Creates ambiguity.
Context Agent
Provides institutional knowledge & legacy docs.
Coding Agent
Generates solutions with subtle bugs.
Execution Agent
Runs code in isolation. Validates functionality.
Evaluator Agent
Post-assessment analysis & scoring.
Four Stage evaluation pipeline
Ambiguity Analysis
The system evaluates how candidates dissect vague requirements. It tracks clarifying questions against a hidden matrix of edge cases, scoring the ability to identify missing constraints before writing a single line of code. We also analyze the precision of language used to define system boundaries.
Monitored Sandbox
Every keystroke and execution is isolated in a deeply instrumented ephemeral runtime. We capture not just the code, but the process.
For Developers
Showcase your ability to lead AI, not just follow syntax. Get graded on judgment, vigilance, and architecture.
- Real-world architectural problems
- Access to complete pedagogical environment
- Detailed feedback report
For Companies
Hire engineers who are force-multipliers. Our evidence-based scoring predicts on-the-job performance in an AI-native world.
- Signal-over-noise scoring
- Full session replay & audit logs
- Customizable agent personas