Agentic AI — A practical beginner's guide

Plain-language explanations, architecture diagrams, a hands-on starter, safety best-practices and a road-map to build simple agents.

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What is Agentic AI? (quick)

An agentic system is a program that can plan and execute multi-step tasks autonomously by calling tools (APIs, scripts, search), saving observations to memory, and re-planning when needed — like a small, focused project manager.

Analogy: the agent is a project manager. Planner = to-do list; Executor = workers/tools; Memory = notes and logs.

Core components

Planner

Decomposes a goal into actionable steps (often via an LLM or rules).

Executor / Tools

Functions or APIs the agent calls (search, email, DB). Keep tool contracts explicit.

Memory

Short-term and long-term storage (arrays, files, vector DBs) used for context and retrieval.

Monitor & Safety

Guardrails, audit logs, human approval gates and policy checks.

High-level loop

A typical run-loop:

User Goal → Planner → Plan (steps) → Executor (tools) → Observations → Memory → Replan / Verify → Planner

Loop until the goal is satisfied or a human intervenes.

Design patterns & best practices

Tool-first: define tool name, input & output schema.
Hierarchical planning: separate high-level planning from low-level execution.
Immutable logs: keep action + result records for replay.
Human-in-the-loop: approvals for destructive or sensitive steps.

Hands-on starter (JS & Python)

Goal: make a tiny agent that searches the web and summarizes results. Start with simulated tools, then swap in real APIs.

Minimal architecture

Planner: LLM returns steps (JSON).
Executor: map steps to local functions (search, summarize).
Memory: keep a list of step outputs.
Loop: run steps, save results, replan on failure.

Node.js (pseudocode)

// agent.js (pseudocode)
const tools = {
  search_web: async (q) => { return [{title:'A',link:'...'}] }, // simulate
  summarize: async (text) => { return 'short summary...' }
};

async function planner(goal, memory) {
  // ask LLM to return JSON steps; here we return a simple plan
  return [{name:'search_web', input: goal}, {name:'summarize', input: '<>'}, {name:'finish'}];
}

async function runAgent(goal) {
  const memory=[];
  const plan = await planner(goal, memory);
  for (const step of plan) {
    if (step.name === 'finish') break;
    const fn = tools[step.name];
    const result = await fn(step.input);
    memory.push({step, result, ts:Date.now()});
  }
  return memory;
}

Python (pseudocode)

# agent.py (pseudocode)
def planner(goal, memory):
    # call LLM to create JSON steps (simulate here)
    return [{"name":"search_web","input":goal},{"name":"summarize","input":"<>"},{"name":"finish"}]

def search_web(q):
    return [{"title":"A","url":"..."}]

def run_agent(goal):
    memory=[]
    plan = planner(goal, memory)
    for step in plan:
        if step['name']=='finish': break
        if step['name']=='search_web':
            r = search_web(step['input'])
        memory.append({'step':step, 'result':r})
    return memory

Tip: test locally with simulated responses before enabling networked tools.

Project ideas

News summarizer agent (search → summarize → combine).
Email assistant (draft → human review → send) — requires sandboxing.
Local file organizer (local-only automation for safety).

Safety checklist

Scope

Define allowed actions clearly.

Human approval

Require approvals for risky tasks.

Logging

Record every action and output.

Policy filters

Filter/deny unsafe prompts and outputs.

Learning path

Learn LLM basics and prompting
Build small LLM utilities (summarize/classify)
Create planner + simulated tools
Add memory (file or vector DB)
Integrate one real-safe tool and add tests
Add logging and human approval gates

Get started — resources

Starter materials will be posted here soon. For now, follow the learning path above and check back later.

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