1
Language Model Layer
The core reasoning engine that powers language understanding, task planning,
and code generation. This is the brain of your AI agent.
GPT-4
Claude
MISTRAL AI
Ollama
Model Selection
💡 Simple Example:
Think of this like the "brain" of a smart assistant. When you ask "Plan a trip to Paris,"
this layer understands your request and breaks it down into actionable steps like booking flights,
finding hotels, and creating an itinerary.
2
Memory & Context Layer
Enables long-term thinking by storing past conversations, documents, and user context
using vector databases for intelligent session management.
Redis
Weaviate
Pinecone
Chroma
Qdrant
LangChain Memory
💡 Simple Example:
Like a diary that remembers everything. If you told your AI agent about your dietary restrictions
last week, it will remember and suggest appropriate restaurants when planning your trip.
It's the AI's "long-term memory."
3
Tooling Layer
Gives the agent the power to act! Connects to APIs, files, browsers, and
external tools for action calling or plugin interfaces.
LangChain Tools
OpenAI Functions
Anthropic Tools
Playwright
Selenium
API Connectors
💡 Simple Example:
These are the "hands" of your AI agent. Just like you use apps on your phone,
the AI uses tools like web browsers to search for flights, calculators to compute costs,
or calendar apps to schedule meetings.
4
Orchestration Layer
Manages agent workflows, coordinates complex logic like task
decomposition, multi-step planning, and multi-agent collaboration.
CrewAI
LangGraph
AutoGen
Swarm
TaskWeaver
💡 Simple Example:
The "project manager" of AI agents. When planning a complex event, this layer breaks
the task into smaller parts (venue booking, catering, invitations) and coordinates
different AI agents to handle each part efficiently.
5
Communication Layer
Makes agents collaborate! Allows agents to talk, delegate, or negotiate
through protocols like A2A or shared memory.
A2A Protocol
MCP
JSON RPC
Message Passing
💡 Simple Example:
Like a WhatsApp group for AI agents! When planning your trip, a travel agent AI
might message a weather AI asking "What's the forecast for Paris next week?"
so it can suggest appropriate activities.
6
Infrastructure Layer
Runs agents at scale! Handles deployment, compute, logging,
observability, and DevOps tools like Docker, Cloud run, ECS.
Docker
Kubernetes
AWS ECS
Google Cloud Run
Azure Container
Vertex AI
💡 Simple Example:
The "computer servers" that run everything. Just like Netflix needs powerful servers
to stream movies to millions of users, AI agents need robust infrastructure to
handle multiple requests simultaneously and reliably.
7
Evaluation Layer
Improves reliability and trust! Tracks errors, hallucinations, and
measures performance through human feedback, prompt evaluation, and feedback loops.
RAGAS
LangSmith
MLflow
PromptLayer
💡 Simple Example:
The "quality control inspector." This layer constantly checks if the AI agent
is giving accurate information, measures user satisfaction, and learns from mistakes
to improve future performance - like a teacher grading and providing feedback.