Bhavin Mistry. Enterprise AI
Agentic AI

Agentic RAG Architecture

Multi-hop query decomposition, dynamic query reformulation, and autonomous citation critique for high-complexity enterprise research.

SYSTEM TOPOLOGY & DATA FLOW ENTERPRISE SPECIFICATION
Client Application RBAC Context Security & Gateway PII Sanitization Semantic Cache Check Rate & Token Budget Inference & Rerank BM25 + Vector Fusion Cross-Encoder Top-5 Grounded Synthesis Telemetry OpenTelemetry

The Core Problem Solved

Traditional one-shot RAG fails when a user question requires aggregating information across disparate systems, comparing temporal periods, or reconciling conflicting data.

When To Deploy This Architecture

Complex enterprise analytical research, multi-document financial audit reconciliation, and cross-system policy verification.

Architectural Components

  • Plan-and-Solve Query Decomposer
  • Tool Registry (Vector DB, SQL Database, REST APIs, Web)
  • Reflection & Self-Correction Evaluator
  • State Management & Memory Store
  • Deterministic Guardrails & Timeout Fence

Data Flow Narrative

Ambiguous query -> Query Decomposition into sub-goals -> Selective Tool Calling -> Reflection check (Is evidence sufficient?) -> Optional recursive refinement -> Final synthesis.

Security & Perimeter Control

Strict sandboxing of all tool execution environments; read-only credentials on data connectors; explicit approval steps before any state-mutating action.

Governance & Telemetry

Graph-based trace recording (LangSmith / OpenTelemetry) capturing each agent reasoning step and decision branch.

Identified Failure Modes & Mitigations

Recursive loop death spirals; tool schema drift; semantic divergence where agent answers a different question than asked.

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