Text Watermarking & Latency Impact Auditor with Claude Agents SDK and Credo AI
Implement a real-time text watermarking latency auditor using Claude Agents SDK and Credo AI to evaluate token generation overhead across quantized model variants. Measure throughput degradation accurately to ensure watermarking adds less than 3% overhead under peak serving loads.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an automated audit agent using Claude Agents SDK and Credo AI governance hooks to measure watermark latency penalties.
How work is evaluated
Evaluates accuracy of measured latency overhead percentage and watermark detection quality.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
Overhead Limit Test
Ensures overhead percentage added by watermarking is under 3%.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Watermark Detection Z-Score
Statistical confidence score of watermark detection. • target: 6 • range: 4-10
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build a watermarking impact analyzer using Claude Agents SDK.
Implement Credo AI compliance checks for watermark detectability and performance overhead.
Measure green/red list logit sampling impact on token generation speed.
Generate automated governance reports on safety overhead metrics.
Reference links and supporting material
Prompts and baseline non-watermarked timing datasets across 500 token streams.
How this agent runs
Evaluates accuracy of measured latency overhead percentage and watermark detection quality.
Challenge input
JSON containing sampling configuration and batch size
Claude Agents SDK
Anthropic's agent framework for structured tools and evaluation workflows
Credo AI
Automates compliance checks and governance reporting for model features
Evaluated output
JSON with measured overhead_percent and detection_z_score
- Ensures overhead percentage added by watermarking is under 3%.
- Statistical confidence score of watermark detection. • target: 6 • range: 4-10
- Benchmark: LLM Watermarking Benchmark
- Watermark Detection Z Score target: 6
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- Credo AI · Required
- Claude Code · Optional
- Anthropic · Optional
- Claude Code · Optional
Evaluation contract
- Overhead Limit Test · Weight 1
- Watermark Detection Z-Score · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.