Move configuration to a YAML file, and make the tuning knobs configurable
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The thresholds Part H says to tune on the real inference hardware were hardcoded, so every adjustment would have been a PR. They now live in a `tuning:` section of a YAML config file, which also replaces flat environment variables as the home for structure (#32). - The file (NISSE_CONFIG, default ./nisse-dev.yaml) carries structure and holds no secrets: credentials are ${VAR} references resolved from the environment, and a reference to an unset variable is an error - The environment still wins: every existing NISSE_* variable overrides its key, and the file is optional, so .env and CI work unchanged - Validation stays fail-fast and reports every problem at once; unknown keys are errors, so a misspelt knob fails at startup instead of silently doing nothing - The config file joins the state store and .env in the check that refuses to start when they sit inside the workspace clone - Tuning covers the executor's turn and failure limits, the consult budget, the delegation caps, the command timeout and the tool output limit, which Workspace now takes as a setting; the defaults are the values the agent already ran with - llm.max_output_tokens reaches both LLM clients from the config docs/Tuning-Guide.md explains the knobs for someone learning local model tuning: how weights, quantization and the KV cache spend VRAM, what each knob does and what goes wrong at either extreme, and an order to tune in. nisse-dev.example.yaml documents every key and is checked by a test so it cannot drift. Closes #32 Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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@@ -20,7 +20,7 @@ from nisse_dev.envfile import load_env_file
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def main() -> int:
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load_env_file(Path(os.environ.get("NISSE_ENV", ".env")).expanduser())
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try:
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config = AgentConfig.from_env()
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config = AgentConfig.load()
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except ConfigError as exc:
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# Fail here rather than twenty minutes into a milestone on the one
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# call that needed the missing value.
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