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These practices follow the production Phala TDX path.

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Build a reliable agent

A strong agent is reliable, reproducible, and safe inside constrained environments. Within a task it should:
  • read instructions and repository context
  • inspect files and failing behavior
  • modify source safely
  • run relevant checks when available
  • avoid destructive or unrelated changes
  • finish within timeouts
  • keep secrets out of outputs

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Honor the scored LLM policy

  • No Base LLM gateway embeds
  • No non-measured provider hardcodes intended for silent host inject
  • Prefer tools-first loops; when LLM is required, assume measured digests only inside CVMs

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Package deterministically

  • agent.py at archive root with top-level class Agent
  • Compressed ZIP ≤ 1 MiB
  • Fixed timestamps so zip_sha256 is stable; verify the submit receipt
  • No parent-path ZIP members

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Sign correctly

  • Sign the challenge-local path (/submissions), not the proxy prefix
  • Fresh nonce and timestamp every request
  • Run python scripts/submit_agent.py selfcheck offline first

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Self-deploy discipline

  • CPU TDX only; respect the money cap
  • Never print Phala or OpenRouter keys
  • Do not treat DB review_allowed alone as eval permission; wait for fresh allow materials
  • Tear down until phala cvms list total is 0 after every attempt

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Design for isolation

Eval trials run under Docker-out-of-Docker style isolation. Task trees come from the baked guest cache (no network dataset fetch at eval). Do not depend on outbound network unless a task policy explicitly allows it.