AI-debugging-agent / openenv.yaml
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Initial changes for ai-debugger
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name: debugops
version: "1.0.0"
description: >
DebugOps β€” an AI Incident Response environment where an agent acts as an
on-call SRE to diagnose and resolve production system failures using
noisy logs, degraded metrics, and multi-step fix sequences.
observation_space:
type: dict
fields:
services:
type: dict
description: "Per-service health status: api, db, cache β†’ healthy | degraded"
logs:
type: list[str]
description: "System log lines (may contain noise and red herrings)"
metrics:
type: dict
fields:
latency: { type: float, description: "Latency in ms" }
error_rate: { type: float, description: "Error rate 0.0–1.0" }
cpu: { type: float, description: "CPU utilisation %" }
time_step:
type: int
description: "Steps elapsed in the current episode"
action_space:
type: discrete
actions:
- restart_api
- restart_db
- restart_cache
- scale_up
- noop
tasks:
- name: simple
description: "Single-service failure; 2-step fix; 15-step budget."
module: tasks.task_simple
max_steps: 15
difficulty: low
- name: multi_service
description: "Two services degrade simultaneously; 12-step budget; extra latency penalty."
module: tasks.task_multi_service
max_steps: 12
difficulty: medium
- name: critical
description: "Memory-leak root cause with misleading logs; SLA penalty; 10-step budget."
module: tasks.task_critical
max_steps: 10
difficulty: high
grader:
module: grader.grader
function: evaluate_episode
score_range: [0.0, 1.0]
deterministic: true
baseline:
module: agent.baseline
function: act
description: "Heuristic agent using log-keyword matching and metric thresholds."
inference:
script: inference.py
env_vars:
- API_BASE_URL
- MODEL_NAME
- HF_TOKEN
required_env:
- name: API_BASE_URL
description: "OpenAI-compatible LLM endpoint"
default: "https://router.huggingface.co/v1"
- name: MODEL_NAME
description: "Model identifier"
default: "Qwen/Qwen2.5-72B-Instruct"
- name: HF_TOKEN
description: "HuggingFace / OpenAI API key"
required: true