Agentdb Causal Explain

Causal Explain

Given a target memory (an episode, a failed test, an outgoing change), traverse the causal graph backwards through edges to surface the chain of preceding events that explain it.

When to use

• "Why did this fail?"
• "What led to the regression?"
• "Trace the dependency chain for X."
• Incident postmortem — surface every step + decision that contributed.

API

agentdb_causal_explain(
  targetMemoryId:  <id>
  maxDepth?:       3
  minConfidence?:  0.5
  edgeWeights?:    'uplift' | 'confidence' | 'product'   // ranking strategy
)

Returns a path or DAG of (node, edge, node) tuples ranked by combined confidence × |uplift|. Each step carries the relation (caused, supersedes, depends-on, etc.) so the explanation reads naturally.

Output shape

Why did "deploy-2026-05-04 failed migration" happen?

  ┌─ skill[migrate-add-not-null-column]   confidence 0.92
  │     ─[supersedes]→ skill[v1: migrate-with-default]
  │     ─[caused]→     episode[long-running migration on 50M rows]
  │                          ─[caused]→ episode[deploy-2026-05-04 failed migration]
  │
  └─ adr[ADR-046: zero-downtime migration policy]   confidence 0.78
        ─[depends-on]→ skill[migrate-add-not-null-column]

Use the investigator agent

For complex traces, dispatch the agentdb-investigator agent (this plugin) — it walks deeper, cross-references with hierarchical memory, and writes a postmortem-shaped report.

Don't

• Don't traverse with maxDepth > 5 casually — graph fan-out gets exponential and the bandit's confidence weights don't compensate.
• Don't ignore the confidence column. A high-uplift edge with confidence 0.3 is gossip, not evidence.