{
  "handle": "AgentTransitionGraph",
  "ref": "AgentTransitionGraph#e470",
  "stub": "e470",
  "semaId": "sema:AgentTransitionGraph#mh:SHA-256:e47085dbeb60c4b6693b562d2ae9dfd0da62dab6f438c0e7873d808446a8c4cf",
  "layer": "Mind",
  "category": "Strategy",
  "gloss": "Model an agent system's actions as a weighted state graph and plan backwards from a goal state",
  "mechanism": "Record metadata from an agentic harness (tool calls, context injections, commands to sub-agents) and build a decision graph in which each edge carries the probability that the system moves from one state to the next. Given a current state A and a desired state D, walk the graph backwards to find the likeliest path and steer the orchestration along it.",
  "invariants": [
    "Edge weights come from observed harness data",
    "State space is bounded enough to inspect"
  ],
  "failureModes": [
    "State space larger than assumed",
    "Distribution shift makes past transitions misleading"
  ],
  "ring": 2,
  "tier": 3,
  "aliases": {},
  "buildsOn": [],
  "usedBy": [
    {
      "handle": "GameTransitionGraph",
      "type": "composes_with",
      "alias": "agent_transition_graph",
      "url": "/knowledge-graph/GameTransitionGraph/",
      "json": "/knowledge-graph/GameTransitionGraph.json"
    },
    {
      "handle": "ExpertInformedOrchestration",
      "type": "composes_with",
      "alias": "graph",
      "url": "/knowledge-graph/ExpertInformedOrchestration/",
      "json": "/knowledge-graph/ExpertInformedOrchestration.json"
    }
  ],
  "sources": {
    "research": [],
    "works": [],
    "homePage": false
  },
  "url": "/knowledge-graph/AgentTransitionGraph/",
  "graphUrl": "/knowledge-graph#AgentTransitionGraph",
  "directory": "/knowledge-graph/patterns.json"
}