Direct user interactions that initiate the AI pipeline through multiple modalities

Automated triggers from enterprise systems, schedules, and IoT/edge sensors

Kafka / RabbitMQ — central nervous system with replay protection & dedup

Multi-layer security: injection detection, PII redaction, RBAC, data classification

Normalizes, classifies intent, rewrites queries, and routes by modality

JSON Schema validation ensures request structure conformity

The cognitive core — ReAct loop with Chain-of-Thought reasoning and self-reflection

Deterministic vs. probabilistic paths — the critical architectural decision

Supervisor, specialist, and evaluator agents collaborate on complex tasks

The LLM's active working space — what it can "see" right now (e.g., 128K tokens)

Non-persistent attention cache — accelerates token generation, cleared per session

Long-term storage that survives across sessions — with write-back policies

6-stage pipeline: ingest → chunk → embed → index → search → re-rank

MCP servers and direct APIs — the standard way AI connects to tools and data

Entity-relationship graph for multi-hop connected reasoning

Exact-result tools — same input ALWAYS produces same output. No LLM involved.

AI-powered tools for understanding, generation, and pattern recognition

Enterprise systems, workflows, notifications — each with circuit breakers

Policy engine, model registry, audit trails, bias monitoring, data lineage

Traces, evals, token tracking, latency monitoring — full-stack visibility

Glass box AI — chain-of-thought traces, source attribution, confidence scores

Final safety: hallucination detection, toxicity, PII, format validation

Human oversight for high-stakes decisions — approve, reject, or modify

Final response via streaming chat, documents, APIs, or triggered actions

GPU clusters, CPU nodes, Kubernetes — with cost implications

Vector DB, PostgreSQL, object storage — all encrypted at rest

Air-gapped / VPN, TLS 1.3, API gateway, service mesh — zero data egress

HSM keys, AES-256, container security, training data pipeline, bias detection