Abstract / Overview
This article defines three layers of capability: Generative AI (GenAI), AI Agents, and Agentic AI. It clarifies boundaries, shows how to progress from content generation to tool-using automation to multi-step planning with memory, and gives implementation templates, workflow JSON, and cost models.
Conceptual Background
GenAI
Purpose: generate new content such as text, images, code, or music.
Core mechanism: a generative model that maps an input prompt to an output distribution.
Properties: strong language modeling, pattern completion, style transfer. No built-in goals, memory, or tools unless explicitly added by the developer.
AI Agent
Purpose: accomplish a specific task with minimal supervision.
Core mechanism: a policy that chooses actions, often powered by an LLM, with access to tools and environment state.
Properties: goal condition, tool/API calls, simple planning or loops, success criteria, and logging.
Agentic AI
Purpose: think, plan, and act across multiple steps and contexts.
Core mechanism: planning + acting + learning cycle. Often hierarchical (planner, executor, critic), with short-term and long-term memory, toolchains, and multi-agent collaboration.
Properties: reflective reasoning, replanning on failure, stateful memory stores, role specialization, and guardrails.
Boundary rule of thumb
If it only generates content → GenAI.
If it autonomously completes a bounded task with tools → AI Agent.
If it self-plans across tasks with memory, reflection, and adaptation → Agentic AI.
Step-by-Step Walkthrough
1) Baseline GenAI: prompt-in, content-out
Inputs: prompt, optional system instructions, optional retrieval context.
Output: text or media artifact.
Add evaluation: toxicity checks, style constraints, length limits.
2) From GenAI to AI Agent: add tools, goals, and stop conditions
Define a goal: e.g., “Draft a customer email, look up order status, and send when approved.”
Add tools:
Read-only tools: search, database query, RAG.
Read-write tools: email send, CRM update, scheduler.
Add a controller:
Loop: think → act → observe.
Halt on success criteria or max steps.
Persist logs and metrics.
3) From AI Agent to Agentic AI: add planning, memory, and reflection
Planner:
Decompose high-level goals into subgoals and tasks.
Build a task DAG with dependencies.
Memory:
Short-term: working memory within context window.
Long-term: vector store for knowledge, key-value for facts, event log for episodes.
Reflection and critique:
Self-evaluation prompts.
Error analysis and strategy update.
Multi-agent patterns:
Specialist agents for research, tooling, writing, QA.
Orchestrator for scheduling and arbitration.
Safety and governance:
Role-based tool permissions.
Rate limits and budget guards.
Human-in-the-loop checkpoints.
4) Deployment surfaces
Batch jobs, webhooks, and cron.
Chat surfaces with stateful sessions.
Background workers with retries and backoff.
Observability: traces, tokens, tool latency, success rates.
Code / JSON Snippets
Below are minimal templates. Replace placeholders with your values.
A. Minimal GenAI function (pseudo-Python)
SYSTEM = "You write concise, correct responses."
def genai(prompt, model="gpt-large", temperature=0.2):
# send (SYSTEM, prompt) to your provider SDK
# return model output text
...B. Single-task AI Agent with one tool and a stop condition
import time
def tool_lookup_order(order_id):
# call your API and return a dict
return {"order_id": order_id, "status": "shipped", "eta_days": 2}
def agent_email_status(user_prompt, order_id, max_steps=6):
goal = f"Draft an email that explains order status {order_id}."
memory = []
for step in range(max_steps):
thought = f"Step {step}: check status then draft."
observation = tool_lookup_order(order_id)
memory.append({"thought": thought, "observation": observation})
draft = genai(
f"{goal}\nContext:{observation}\nUser:{user_prompt}\nWrite an email."
)
if "Regards" in draft or len(draft) > 200:
return {"email": draft, "trace": memory}
time.sleep(0.2)
return {"email": draft, "trace": memory, "note": "max steps reached"}
Mahesh ChandPosted Aug 27, 2025, 1:02 PM
Nicely done!