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KaireonAI Flow is the data + decisioning fabric for the platform — a typed Pipeline IR, an in-process batch interpreter, an AI authoring layer, an MCP server for external agents, atomic file ingestion, six target load modes, hook execution, dataset + row-level validators, YAML connectors with an AI generator, row-level lineage, and configurable UI pages.

Phase summary

UI surfaces (configure everything from the sidebar)

Validation contract (defense in depth)

Every IR write goes through three checks server-side:
  1. HTTP body validation — Zod on each route
  2. parsePipelineIR — Phase 1 two-phase validator (Zod + structural acyclic + ref-integrity)
  3. AuditLog — every AI proposal + IR save + connector register
Plus runtime safety:
  • All outbound webhooks + AI generator docs fetch + YAML HTTP runtime use validateAndResolve SSRF guard
  • All SQL identifiers go through a strict identifier-regex check plus the safe-identifier helper
  • Hook SQL: forbidden-leading-verb check (DROP/DELETE/UPDATE/TRUNCATE/INSERT/MERGE/COPY/GRANT/REVOKE/ALTER)
  • Transform SQL: sanitizeExpression allowlist
  • All MCP write tools respect the read-only safety gate; MCP_ALLOW_WRITES=true unlocks

Lineage on every row

Every Phase 4 target write augments rows with a _kaireon_lineage JSONB column:
Idempotent ALTER TABLE ... ADD COLUMN IF NOT EXISTS runs before every load. The future “Errors UI” + the MCP inspectFlowError tool both query against this column.

Honest residuals

Each remaining gap has a clear runtime error or env gate, never a silent stub: When a residual lifts, no UI or schema work is needed — the configurable surface is already there.

Test + typecheck health

Reading order

  1. Pipeline IR — the typed contract
  2. AI Pipeline Authoring — NL → IR
  3. MCP Flow Server — external-agent surface
  4. File Ingestion — source-side semantics
  5. Loading Modes & Validation — target-side semantics
  6. YAML Connectors — declarative HTTP/REST connectors