Agentic AI: How Autonomous Software Is Changing the Way Startups Operate

Day 1: Agentic AI

Mohammad Ashraful Islam - CEO Devs Core

Ashraful Islam

21 June, 2025

Welcome to Day 1 of Agentic AI Series by Devs Core

“You don’t need an assistant. You need a teammate who thinks.”
That’s what Agentic AI promises—and it’s already happening.

🎯 Introduction: Why This Blog Series?

Let’s face it—every founder is drowning in to-dos.

Marketing campaigns. Data dashboards. Customer escalations. Product feedback. Internal ops.
Now imagine if you could hire an AI teammate to think like you, work like you, and act independently on your behalf—across tools like Notion, Slack, HubSpot, and your internal API.

That’s not wishful thinking.
That’s Agentic AI.

In this blog series, we’ll explore:

  • What Agentic AI is

  • Why it’s a shift, not just a trend

  • How to build your first agent

  • Real-world examples, tools, and resources

This post kicks off your journey with everything you need to understand Agentic AI from a product and engineering lens.

đź§  What Is Agentic AI?

Traditional AI = Give it a prompt, it replies.
Agentic AI = Give it a goal, it figures out how to achieve it, step by step.

At its core, agentic AI is goal-directed software powered by LLMs. Instead of waiting for you to act, the AI:

  • Sets sub-goals

  • Chooses tools

  • Plans workflows

  • Executes them over time

  • Adapts based on context and memory

Comparison between traditional reactive AI and goal-driven agentic AI systems in software development

⚙️ The Core Components of an Agent

To understand how it works, let’s break it down:

ComponentFunction
PlannerBreaks down the main goal into smaller tasks
ExecutorExecutes each task (e.g., API call, database query)
MemoryStores past actions, results, and failures
Tool UseCalls APIs, plugins, or functions (like Zapier or LangChain tools)
ReflectorEvaluates outcomes and self-corrects

Think of it like a startup team with a PM, dev, ops, and QA—all inside one AI loop.

Diagram showing planner, executor, memory, tool use, and reflector components of an agentic AI system

đź§Ş Real Use Case: An Email Agent for Startup Ops

Let’s build something real. Here’s a LangGraph-based Agentic Email Assistant:

  • Checks your Gmail every morning

  • Summarizes key messages

  • Flags customer complaints

  • Suggests replies

  • Sends a Slack digest

📎 Check out this tutorial to learn how to build this!

This agent:

  • Has memory

  • Makes decisions (what’s urgent)

  • Uses tools (Gmail API, Slack API)

  • Runs daily without supervision

🔥 Now imagine building this for:

  • Daily sales reporting

  • Ops escalation alerts

  • Content republishing

  • Inventory monitoring

You’re not building chatbots. You’re building thinking software.

đź§° Tools to Start Prototyping Agents

Here are the top agentic frameworks developers use:

ToolBest ForWebsite
LangGraphMulti-step agents with graph-based logiclanggraph.dev
Microsoft AutoGenRole-based, multi-agent collaborationGitHub Repo
CrewAIOrchestrating agents with specific job rolescrewai.io
GPTScriptFast prototyping of agents with codegptscript.ai
Grid view of AI agent tools including LangGraph, AutoGen, CrewAI, and GPTScript for building agentic AI workflows

🤖 Agentic AI vs RAG vs Traditional Automation

CategoryBehaviorLimitation
RAG (Retrieval-Augmented Generation)Answers better using external docsNo autonomy or decision-making
Zapier/IFTTTExecutes static tasks on triggerNo logic, planning, or memory
Agentic AIPlans and executes toward goalsComplex, but flexible and evolving

Agentic AI isn’t replacing those—it’s layering on top of them.

đź’Ľ Why Should Startups and Tech Teams Care?

Because it’s the next platform shift—like mobile or cloud was.

You can:

  • Build faster with fewer people

  • Automate growth and ops

  • Create scalable workflows that improve over time

  • Integrate AI across every surface: product, marketing, support, engineering

✨ Devs Core’s Take

We’ve used agentic frameworks to:

  • Automate 50% of internal reporting

  • Prototype finance assistants for SaaS founders

  • Build support agents that reduce ticket volume

If you’re a startup scaling fast but short on time—agentic AI is your new weapon.

Workflow diagram of agentic AI performing tasks: observe, plan, execute, and reflect using AI automation tools

đź§  FAQs About Agentic AI

  • Yes. Several tools like LangGraph and AutoGen are stable, used by enterprises, and open source.

  • Python, API integration, and a basic understanding of LLMs. Or work with a team like Devs Core.

  • Start with back-office ops, marketing automations, or internal tools—low risk, high reward.

âś… Your Next Steps

🎯 Want to explore Agentic AI for your startup?
Let us help. We’ve built AI-powered systems that:

  • Save 40+ hours/month

  • Improve customer experience

  • Adapt with your business over time

👉 Book a free consultation
👉 Or build your first agent using this LangGraph tutorial

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