The Agentic AI Cheat Sheet

Everything on one page: the six components every agent stack has, the four kinds of memory, the loop they run in, and the places agents actually break in production. Free PDF, print it or keep it open while you build.

Agentic AI Cheat Sheet

Components, memory, and the loop that runs them — a one-page reference

SkyTrainings

skytrainings.in

1What Agentic AI Is

A chatbot answers. An agent decides, acts, checks the result, and decides again. That loop is the whole difference — an LLM with tools, memory and autonomy wrapped around it.

Key capabilities

Autonomous decision making
Tool use and function calling
Memory and context across steps
Planning and reasoning
Self-correction on failure

2Where It Sits

Artificial Intelligence

Rule systems, Search, Planning

Machine Learning

Supervised, Unsupervised, RL

Deep Learning

Neural Networks, Transformers

Large Language Models

GPT, Claude, Gemini, Llama

Agentic AI

Tool Use, Memory, Autonomy

Each layer contains the ones below it.

3The Agent Loop

Two failure points.The loop back from “Goal met?” spins forever if the stop condition is weak. The “Tool needed?” branch burns budget when the model picks the wrong tool repeatedly.

4Core Components

1OrchestratorThe brain

Decides the next step, routes work, owns the stop condition

2Model LayerThe reasoning

Interprets the goal, produces the plan, picks the tool

3Tools / APIsThe hands

Search, code execution, databases, third-party calls

4MemoryThe context

Short-term buffer, long-term retrieval, what survives a run

5GuardrailsThe limits

Validates arguments, blocks destructive or out-of-scope actions

6ObservabilityThe evidence

Traces every step so a failure is explainable afterwards

5Memory Architecture

Short Term

Conversation buffer, recent turns

Context window management

Long Term

Vector DB, knowledge base

This is where RAG lives

Episodic

Past actions, outcomes, failures

Stops repeated mistakes

Working

Current task state, intermediates

Scratchpad for this run

6Tool Layer

Web Search

Beyond the cutoff

Code Executor

Sandboxed runs

SQL / Database

Structured queries

File Reader

Docs into context

API Caller

Internal + 3rd party

Knowledge Base

Your own documents

Few, well-described tools beat many vague ones — descriptions compete for the same context as the task.

7A Sane Build Order

1
Define the stop condition
Decide what "done" means before writing any loop
2
Start with one tool
Prove the call-observe cycle end to end
3
Add memory deliberately
Pick the type the task needs, not all four
4
Wrap it in guardrails
Validate arguments, cap iterations and spend
5
Instrument before scaling
You cannot debug a loop you cannot trace

8Words People Mix Up

ReAct Reason-then-act. The pattern most tool-using agents trace back to (2022).

RAG Retrieval. A technique an agent uses, not a kind of agent.

Orchestration Coordinating steps or multiple agents. Not the reasoning itself.

MCP A protocol for exposing tools to a model in a standard way.

Guardrails Constraints on what an agent may do. Not the same as prompt rules.

9Where Agents Actually Break

No clear stop condition, so the loop never terminates
Poor tool selection burning budget on wrong calls
Too many tools, so descriptions crowd out the task
No tracing, so nobody can explain the failure
Memory added before observability exists
Unvalidated tool arguments reaching real systems

Useful test:when someone says “we built an agent,” ask what its stop condition is. No clear answer usually means a chatbot with a function call bolted on.

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