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Phase 3 Track A — Telco Troubleshooting Agentic Challenge
Outer Banks 5 (solo team) — ITU × Zindi 2026
Files in this repository
| File | Purpose |
|---|---|
code.zip |
The submission archive — extract and follow README.md inside |
TECHNICAL_REPORT.pdf |
Technical report describing the agentic workflow |
What's inside code.zip
code.zip (37 KB, 12 files)
├── README.md reproduction instructions
├── run.sh entry shell wrapper
├── run.py driver — iterates scenarios, writes result/
├── requirements.txt 4 pip packages
├── models/
│ └── deploy.sh vLLM startup for Qwen3.5-35B-A3B
└── src/
├── runtime_logger.py verbatim from spec
├── phase2_pipeline.py 16-rule deterministic solver
├── sandbox.py 22-endpoint HTTPS client
├── tools.py 15 tools + analytic helpers
├── prompts.py unified agent prompt
└── agent.py Qwen tool-calling loop with safety nets
Architecture summary
Qwen3.5-35B-A3B is the orchestrating brain; fifteen specialised tools
are its hands. The brain calls recommend_action() (the solver) first
on every scenario, reads the routing flag, and selects one of three
investigation protocols: accept the solver's recommendation directly,
refine a binary geometric choice via select_azimuth(), or freely
explore the 24-option remediation menu via a fault-specific
analyse_* tool.
Six layers of defence (streamed outputs, bounded loop, empty retry, tier-aware heuristic fallback, truncation safety net, last-ditch direct solver call) ensure every scenario produces a valid non-empty answer in well under the 5-minute time-discount cap.
See TECHNICAL_REPORT.pdf for the full architecture, tool catalogue,
and reliability design.
How to run (summary)
pip install -r requirements.txt
$EDITOR models/deploy.sh # set MODEL_PATH to Qwen3.5-35B-A3B weights
bash models/deploy.sh # wait for "Application startup complete"
bash run.sh --input test_phase3.json --output result
Outputs land in result/: results.csv, traces.json, runtime.json.