Last updated on February 3rd, 2025 at 02:16 pm
Most AI systems today are like really smart assistants—they give you recommendations, but they still wait for you to act.
But what if AI could make decisions and take actions on its own? That’s what Agentic AI is about. It doesn’t just respond—it gets things done.
Think about customer support. Most AI chatbots just answer FAQs. But an agentic AI could:
- Refund an order if it sees a customer is eligible.
- Reschedule a missed appointment without human intervention.
- Detect a supply chain issue and automatically reorder stock.
This shift from passive AI to active AI is huge. So, how do you actually build something like this? Here’s what matters.
Step 1: Define the AI’s Role (It Can’t Be a Free-for-All)
Before you even think about models or data, define what decisions it should own.
If you’re building an AI-powered IT helpdesk, for example:
- Should it just answer tech questions, or can it reset passwords?
- Can it proactively fix server issues, or does a human need to approve?
- How much autonomy is too much?
Get clear on the boundaries—otherwise, you’ll end up with an AI that either does too little or too much in ways you don’t want.
Step 2: Give It Memory (Otherwise, It’s Just Guessing)
For AI to act intelligently, it needs context. If a customer complains, the AI should remember:
- Have they had this issue before?
- Are they a VIP customer?
- What solutions have worked in the past?
This is where vector databases (like Pinecone) and feature stores (like Feast) come in. They allow AI to recall past interactions instead of treating every situation like a blank slate.
Without memory, AI will just repeat the same mistakes—like a bad customer service rep who asks for your account number every single time.
Step 3: Teach It to Make Decisions (Not Just Predict)
Traditional AI models predict outcomes (e.g., “This customer is likely to churn”). But agentic AI needs to decide what to do next.
To make this work, you’ll need:
- Rules (e.g., “If refund is under $50, process it automatically”).
- Policies (e.g., “Always escalate VIP issues to a human”).
- Reinforcement learning (so it learns what works over time).
This is where tools like LangChain or CrewAI help structure decision-making.
Step 4: Connect It to Systems (So It Can Actually Do Things)
No matter how smart your AI is, it’s useless if it can’t take action.
That means integrating it with APIs—for example:
- A finance bot should be able to approve expense reports.
- A healthcare AI should be able to schedule appointments.
- A supply chain AI should be able to reroute shipments.
Without this, you just have a really smart chatbot—not a real AI agent.
Step 5: Let It Learn (So It Doesn’t Keep Making the Same Mistakes)
Even the best AI won’t be perfect at launch. But the real power comes when it improves over time.
For example, if an AI helpdesk agent keeps escalating certain issues to a human, maybe it should learn how to handle those cases itself.
The key is building feedback loops—where AI gets corrected, logs those corrections, and improves with every cycle.
Final Thought
The biggest mistake companies make with AI? They build models that predict but don’t act.
Agentic AI changes that—it thinks, decides, and executes.
The real question is: What’s one business process you’d automate today if AI could handle it end-to-end?
Let’s discuss.