{
  "schema_version": "newruntime-agent-readable-v0.1",
  "type": "raw_signal",
  "id": "tg-2614",
  "slug": "harvey-trains-legal-agent-with-applied-compute",
  "title": "Harvey trained a legal agent with an applied compute loop",
  "description": "Harvey describes domain experts, task environments, evaluation, and iterative training as one system for legal agent performance.",
  "observed_at": "2026-07-03",
  "why_it_matters": "High-stakes vertical agents need expert-designed tasks and feedback loops around the model rather than a domain prompt added at deployment time.",
  "novelty": "notable",
  "verification_level": "source-inspected",
  "signal_type": "field-report",
  "evidence_kind": "mixed",
  "status": "published",
  "telegram_message_id": 2614,
  "telegram_url": "https://t.me/qwgai/2614",
  "topics": [
    "vertical-agents",
    "evals",
    "legal-ai"
  ],
  "entities": [
    "Harvey"
  ],
  "related_patterns": [],
  "source_urls": [
    "https://harvey.ai/blog/training-a-legal-agent-with-applied-compute"
  ],
  "import_batch": "telegram-2026-07-17-v1",
  "routes": {
    "html": "https://newruntime.com/signals/harvey-trains-legal-agent-with-applied-compute/",
    "markdown": "https://newruntime.com/signals/harvey-trains-legal-agent-with-applied-compute.md",
    "json": "https://newruntime.com/signals/harvey-trains-legal-agent-with-applied-compute.json"
  },
  "source_format": "telegram-export-normalized-json"
}