RLM + Agentic RAG System Architecture

01 · Input & Interface Layer

💬

User Interface

Chat / API / Voice / SDK endpoints accepting natural language queries and task specifications

REST API WebSocket SDK

📋

Query Preprocessor

Intent classification, query decomposition, entity extraction and context injection

NER Intent Decompose

🗂️

Session & Context Manager

Maintains conversation history, episodic memory, session state and user preference profiles

History Episodic Profile

⬇ Structured Query + Context ⬇

02 · Agentic Orchestration Core

🧠

Agent Orchestrator (LLM Core)

Central reasoning engine powering multi-step planning, tool selection, sub-agent delegation and dynamic workflow generation. Maintains the agent loop: Observe → Think → Act → Reflect.

ReAct Loop Tool Use Chain-of-Thought Streaming

📐

Task Planner

Decomposes complex tasks into DAG of sub-tasks, assigns to specialized agents, manages dependencies and parallel execution

DAG Scheduler

⬇ Sub-task Dispatch ⬇

03 · Specialized Agent Pool

🔍

Retrieval Agent

Executes semantic search, hybrid retrieval, HyDE query expansion and re-ranking across knowledge stores

HyDE Re-rank

⚙️

Tool-Use Agent

Executes external tools: web search, code interpreter, calculators, APIs, databases and file systems

Code Exec Web

✅

Critic / Verifier Agent

Validates facts, checks logical consistency, detects hallucinations and scores output quality for RL feedback

Factcheck Scores

✍️

Synthesis Agent

Combines retrieved context with reasoning trace to generate coherent, grounded, cited responses

Grounded Citations

⬇ Retrieval Queries ⬇

04 · Retrieval-Augmented Generation Pipeline

🔢

Embedding Engine

Multi-modal embedding generation (text, code, image) via dense + sparse encoders. Supports bi-encoder & cross-encoder

Dense Sparse BM25 Multi-modal

🗃️

Vector Store

ANN index (HNSW/IVF) over document embeddings. Supports metadata filtering, namespace routing and CRUD operations

HNSW Pinecone Weaviate

📊

Re-Ranker

Cross-encoder re-ranking of top-K candidates using relevance scores, MMR for diversity and query-document alignment

Cross-Enc MMR

📎

Context Assembler

Packs retrieved chunks into LLM context window with deduplication, truncation strategy and source attribution

Dedupe Attribution

⬇ Retrieved Context ⬇ · ⬆ RL Feedback Signal ⬆

05 · Reinforcement Learning Model (RLM) Layer

RL TRAINING & INFERENCE LOOP

🏆

Reward Model

Trained RLHF/RLAIF reward model scoring responses on helpfulness, accuracy, safety and format compliance

RLHF RLAIF PPO

🎯

Policy Model (Actor)

Fine-tuned LLM policy optimized via PPO/GRPO. Generates actions (retrieve / reason / respond) based on state observations

GRPO LoRA Actor

📈

Value Function (Critic)

Estimates expected cumulative reward from current state. Provides advantage estimates to stabilize policy gradient training

GAE Baseline

🔄

Experience Replay

Stores (state, action, reward, next_state) tuples in priority replay buffer for off-policy training and batch updates

PER Buffer

State (query+context)

→

Policy → Action

→

Environment

→

Reward Signal

→

Gradient Update

⬇ Knowledge Indices ⬇

06 · Knowledge & Data Layer

📚

Document Corpus

Raw documents, PDFs, web pages, code repos. Chunking pipeline with sliding windows, semantic splitting and metadata tagging

Chunking Markdown

🌐

Graph Knowledge Base

Entity-relation graph for multi-hop reasoning. Neo4j / GraphRAG enabling structured traversal alongside vector search

Neo4j Multi-hop

⚡

Cache & Short-term Memory

Semantic cache (Redis) for frequent queries, working memory for current agent trajectory and intermediate reasoning steps

Redis Working Mem

🧬

Long-term Memory Store

Persistent episodic + semantic memory. Enables RL agents to recall past episodes, user preferences and successful strategies

Episodic Semantic

🌍

External Data Sources

Live APIs, web search, SQL/NoSQL databases, real-time data feeds and file system connectors

APIs SQL Live

⬇ Final Response ⬇

07 · Output, Safety & Observability

🛡️

Safety & Guardrails

Input/output filtering, toxicity detection, PII redaction, policy enforcement and jailbreak prevention

PII Toxicity

📤

Response Generator & Formatter

Final answer synthesis with citation rendering, format adaptation (markdown/JSON/HTML), streaming token output and confidence scoring

Citations Streaming Confidence Structured JSON

📊

Observability & Tracing

Full trace logging (LangSmith/Phoenix), latency metrics, token usage, RL reward tracking, A/B eval dashboards

LangSmith OTEL

🔃

Feedback Loop

Collects human feedback, thumbs up/down signals, implicit quality indicators — feeds back into RL reward model and RLHF dataset

HITL RLHF Data

Legend — Component Categories

Core LLM / RAG Pipeline

Agent Orchestration / RL Policy

Retrieval & Search

Knowledge Storage / Output

RL Training / Feedback

Safety & Guardrails