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Agentic AI Architecture

Agentic AI is an architecture where an LLM acts as an autonomous agent. It plans and executes multi-step tasks with memory and tools.

Unlike a classic prompt→response app, an agentic system: - Sets sub-goals and decomposes tasks - Interacts with external tools / APIs - Iterates through a sense → plan → act → observe loop - Manages both short-term and long-term memory

Sections

File Topics
Fundamentals & Components What agentic AI is, core components, ReAct loop, and how it differs from a plain LLM app
Multi-Agent Patterns Main multi-agent patterns (Supervisor, Hybrid, BDI, Neuro-Symbolic) and coordination
Memory & RAG STM/LTM, practical RAG pipeline, vector DB choices, retrieval methods
Tool Integration & Prompting Function calling, tool registry, and prompting strategies (CoT, ReAct, ToT)
LLM Config & Security Model settings, model selection, guardrails, and security threats like prompt injection
Testing & Observability Metrics, test layers, observability, failure modes, production checklist
Agentic Search & Context Engineering (2025+) Context engineering vs prompt engineering, multi-source retrieval, context failure modes, compression strategies, GraphRAG, governance
Vectorless RAG Reasoning-based retrieval, hierarchical tree index, PageIndex, traditional vs vectorless comparison, hybrid approach

Key Frameworks

Framework Maintainer Strength Typical Use Case
LangChain LangChain Inc. Tool use, chains, prompts, RAG Chatbots, document Q&A, single-agent loops
LangGraph LangChain Inc. Stateful multi-agent graphs, cycles Reflection loops, complex workflows
LlamaIndex LlamaIndex Enterprise data integration, RAG Knowledge assistants over large corpora
AutoGen Microsoft Multi-agent group chats, code execution Planning, multi-agent collaboration
CrewAI CrewAI Role-based agent teams Business process automation
Semantic Kernel Microsoft .NET + Python, enterprise plugins Enterprise AI assistants

Choosing a framework: - Single agent with tools → LangChain - RAG over a large corpus → LlamaIndex - Multi-agent with group chats → AutoGen - Complex stateful workflows (reflection, branching) → LangGraph

Quick Start: Implementation Checklist

  1. Define Goals — clearly state objective and scope
  2. Select Components — LLM + vector DB + tools
  3. Design Architecture — perception -> planner -> executor -> memory -> observer
  4. Prototype Agent Loop — minimal ReAct with 1-2 tools
  5. Integrate RAG — embedding model + vector store
  6. Add Memory — LTM via vector DB, define promotion rules
  7. Implement Orchestration — supervisor logic or workflow engine
  8. Setup Prompt Strategy — system prompt, tool schemas, few-shot examples
  9. Configure LLM — temperature, token limits, loop caps
  10. Add Guardrails — loop limits, input sanitization, logging
  11. Test Thoroughly — unit, integration, scenario, adversarial
  12. Monitor & Iterate — observability, logs, metrics, refinement

Architecture Overview

User Input
    │
    ▼
┌───────────────────────────────────────────────┐
│                  Agent Core                   │
│  Perception → Planner → Executor → Observer   │
│         ↕ Short-Term Memory (Context)         │
└───────────────┬───────────────────────────────┘
                │
       ┌────────┴────────┐
       ▼                 ▼
Long-Term Memory     External Tools
 (Vector DB + RAG)    (APIs / Code)

See also