decision

/veritasreason:decision

Full decision lifecycle management. Usage: /veritasreason:decision <sub-command> [args]

---

record <category> "<scenario>" "<reasoning>" <outcome> <confidence>

Record a decision with full context.

from veritasreason.context import AgentContext

ctx = AgentContext(decision_tracking=True)
decision_id = ctx.record_decision(
    category=category,        # "loan_approval", "deployment", "hiring"
    scenario=scenario,        # natural-language situation description
    reasoning=reasoning,      # why this decision was made
    outcome=outcome,          # "approved", "rejected", "deferred"
    confidence=float(confidence),
    entities=entities or [],
    decision_maker="ai_agent",
    valid_from=valid_from,    # optional ISO date string
    valid_until=valid_until,
)

Output: Decision <decision_id> recorded | <category> | <outcome> (conf: 0.95)

---

query "<question>" [--hops N] [--hybrid]

Query decisions using natural language with multi-hop graph traversal.

from veritasreason.context import AgentContext

ctx = AgentContext(decision_tracking=True, advanced_analytics=True)
results = ctx.query_decisions(
    query=question,
    max_hops=int(hops) if hops else 3,
    include_context=True,
    use_hybrid_search="--hybrid" in args,
)

For structured lookups use DecisionQuery:

from veritasreason.context.decision_query import DecisionQuery
dq = DecisionQuery(graph_store=ctx.graph_store)
# dq.find_by_category(category, limit=100)
# dq.find_by_entity(entity_id, limit=100)
# dq.find_by_time_range(start, end, limit=100)
# dq.multi_hop_reasoning(start_entity, query_context, max_hops=3)
# dq.trace_decision_path(decision_id, relationship_types)
# dq.analyze_decision_influence(decision_id, max_depth=3)

Return: | ID | Category | Scenario | Outcome | Confidence | Timestamp |

---

precedents "<scenario>" [--category <cat>] [--advanced] [--hops N] [--as-of <date>]

Find similar past decisions using hybrid semantic + structural + vector search.