Mind · Strategy
AgentTransitionGraph#e470
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
Failure modes
- State space larger than assumed
- Distribution shift makes past transitions misleading
Used by
Identity
The name is the hash of the definition. Change a word and it becomes a different pattern.
- Sema ID
sema:AgentTransitionGraph#mh:SHA-256:e47085dbeb60c4b6693b562d2ae9dfd0da62dab6f438c0e7873d808446a8c4cf- Reference
AgentTransitionGraph#e470- Ring
- 2
- Tier
- 3