Cyber Threat Intelligence Triage Agent using Claude Agents SDK and Pydantic AI
Advanced APT hacking campaigns targeting financial infrastructure demand rapid multi-modal forensic triage and automated mitigation recommendations. Build an autonomous threat triage agent with the Claude Agents SDK and Pydantic AI to ingest network telemetry, evaluate indicator confidence, and trigger firewall rule updates. Achieve 95% accuracy on incident severity classification and automated response generation.
What you are building
The core problem, expected build, and operating context for this challenge.
Develop an autonomous forensic triage agent using Claude Agents SDK and Pydantic AI to analyze cyber telemetry and automate incident containment.
How work is evaluated
Tests agent ability to correctly classify APT threat severity and generate valid containment payloads.
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.
schema_validation_test
Verifies output conforms to required Pydantic model
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
severity_accuracy
Accuracy score of assigned incident severity • target: 0.95 • range: 0-1
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 type-safe telemetry parser with Pydantic AI models
Orchestrate forensic analysis workflows using Anthropic Claude Agents SDK
Construct dynamic mitigation rule actions based on attack vectors
Evaluate APT threat indicators with high confidence and minimal false alarms
Reference links and supporting material
Synthetic telemetry dataset representing APT intrusion attempts against core banking infrastructure.
How this agent runs
Tests agent ability to correctly classify APT threat severity and generate valid containment payloads.
Challenge input
JSON telemetry log with PCAP highlights and netflow data
Claude Agents SDK
Agentic framework for Anthropic models
Pydantic AI
Guarantees structured telemetry output
Evaluated output
Pydantic JSON model with severity, confidence, and firewall blocking parameters
- Verifies output conforms to required Pydantic model
- Accuracy score of assigned incident severity • target: 0.95 • range: 0-1
- Benchmark: CyberThreat-Eval
- Severity Accuracy target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Pydantic AI · Required
- Claude Code · Optional
- Claude Code · Optional
- Claude 4.1 Opus · Optional
- Pydantic AI · Required
Evaluation contract
- schema_validation_test · Weight 1
- severity_accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.