An engine built for AI from the first line of code.

-- One connection. One auth token. Three systems other databases need.

-- 1. SQL join + vector similarity, in the same SELECT
SELECT o.id, o.total, c.name,
       COSINE_SIMILARITY(o.notes_vec, EMBED('likely fraud pattern')) AS risk
  FROM orders o
  JOIN customers c ON c.id = o.customer_id
 WHERE o.status = 'flagged'
 ORDER BY risk DESC
 LIMIT 20;

-- 2. Graph hop, same session — vector similarity as an edge predicate
MATCH (o:Order {status:'flagged'})-[:SIMILAR_TO > 0.85]->(prior:Order)
MATCH (prior)-[:PLACED_BY]->(c:Customer)
RETURN o.id, prior.id, c.name LIMIT 20;

-- 3. A durable agent already declared on this table fires on commit —
--    no external orchestrator, no queue.
CREATE AGENT fraud_triage
  PERSONA 'aidb-assistant'
  TASK 'Investigate the flagged order and recommend hold or release.'
  ON INSERT INTO orders WHERE status = 'flagged'
  WITH (max_iterations = 3, timeout_seconds = 90);

-- 4. Every run lands hash-chained in the audit ledger — tamper-evident,
--    self-hosted, never leaves your box.
SELECT run_id, agent_name, started_at, verified
  FROM _system_agent_runs
 WHERE agent_name = 'fraud_triage'
 ORDER BY started_at DESC
 LIMIT 5;