Global Tech Talent Evaluator Agent
With software engineering job openings surging by 30% in 2026, tech firms need more efficient ways to screen global talent while remaining compliant with UK and US transparency laws. You will build a technical recruitment agent using the Claude Agents SDK that conducts simulated voice screenings and technical interviews. This agent will utilize Claude Sonnet 4.6.6 for its high-reasoning capabilities and interaction design. To ensure transparency, you will integrate Alibi Explain to generate post-hoc explanations for why specific candidates were advanced to the next round. The agent will use Retell AI to handle real-time voice interactions with candidates, while BoTorch is employed to optimize the scheduling and ranking of candidates based on multidimensional skill sets. You will also use Llama 3.3 70B as a secondary evaluator to provide a 'second opinion' and reduce model-specific bias.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an explainable technical recruiting system using Claude Agents SDK and Retell AI voice automation to handle high-volume tech hiring.
How work is evaluated
Candidates are evaluated based on their technical response accuracy and the clarity of the Alibi Explain report.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Latency Check
Voice response latency must be under 800ms
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Optimization Efficiency
BoTorch improvement over random selection • target: 2.5 • range: 1-5
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Implement the Claude Agents SDK for stateful interactions and computer-use capabilities in interview sims
Design explainability pipelines with Alibi Explain to provide transparency for AI-driven hiring decisions
Integrate Retell AI SDK for high-fidelity voice synthesis and streaming responses in screening calls
Use Llama 3.3 70B via a side-by-side evaluator pattern to audit Claude's technical assessments
Leverage BoTorch to solve multi-objective optimization problems in talent pipeline management
Construct a secure tool-use environment for the agent to access candidate CVs and GitHub metrics
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
Candidates are evaluated based on their technical response accuracy and the clarity of the Alibi Explain report.
Challenge input
Agent decision log and candidate metadata
Alibi Explain
AI Engineering Tooling · Refactoring + Review
Retell AI
Voice AI for phone calls
BoTorch
AI Workflow Automation · Workflow Runners
Evaluated output
Alibi explanation object and summary
- Voice response latency must be under 800ms
- BoTorch improvement over random selection • target: 2.5 • range: 1-5
- Optimization Efficiency target: 2.5
- 1 public reference case
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Alibi Explain · Required
- Retell AI · Optional
- BoTorch · Optional
Evaluation contract
- Latency Check · Weight 1
- Optimization Efficiency · Weight 1
Metrics
- Optimization Efficiency · Target 2.5
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: Agent decision log and candidate metadata
Output: Alibi explanation object and summary
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.