Architecture
Reference architecture for production agentic AI
Sources -> Data House -> Models -> Orchestrator -> WebKit tools -> HITL + Sessions with governance, validation, and full traceability.
Sources
Documents, video, audio, sensors, and enterprise systems.
Data House
Ingestion, storage, indexing, governance, datasets.
Models
Reasoner LLMs, SLMs, vision/audio/sensor models.
Orchestrator
Agent workflows, validation gates, traces, safety policies.
WebKit Tools
ERP/CRM connectors, tool registry, safe structured actions.
HITL + Sessions
Review queues, corrections, rewards -> interval training.
Why this architecture works
It is designed around controllability: safe actions, evidence grounding, and audit logs.
Grounding
- Retrieval from Data House keeps outputs tied to your data (RAG + metadata).
- Structured schemas reduce ambiguity and make actions machine-safe.
Control
- WebKit tools enforce action boundaries with permissions and schemas.
- Orchestrator adds validation gates and human approval where needed.
Learning loop
- Sessions store traces, outputs, and corrections as training/eval data.
- Interval training improves accuracy while regression tests prevent drops.
Architecture review
Want an architecture review for your environment?
We will map your systems, data, and constraints, then propose a deployment and integration blueprint.