From the Author’s Desk
In my workflows today, I don’t just ask AI for answers. I give AI its goals. AI plans, sequences tasks, checks its own progress, and moves forward without waiting for another prompt. That shift from responding to acting is where agentic AI begins.
Last week we explored incentives and how AI optimizes for the signals it’s given. This week, we’ll unpack what that really means before you let AI operate on your behalf.
Autonomy is powerful. Accountability is still human.
When AI Starts Executing
Most AI tools today are reactive. You give an instruction, and they return a response. The interaction ends there.
Agentic AI changes that dynamic. Instead of responding to one prompt, it works toward a goal. It can plan the steps, choose tools, execute tasks, review progress, and adjust when needed.
In simple terms, it moves from generating answers to managing outcomes.
For example - instead of saying, “Write a campaign email,” you might say, “Launch a product campaign for our new feature.”
🪄An agentic system can define the audience, draft messaging, generate variations, suggest subject lines, and refine the output before handing it back. It is not just writing. It is coordinating multiple actions toward a result. This is the shift!
The Mindset Shift: Look Past the Output
With traditional AI, you judge the response.
With agentic AI, you judge the action.
The question shifts from “Is this output good?” to “Should this system be allowed to execute this?” You are no longer just reviewing text. You are defining control.
Before you let AI move faster, you need to be clear on where it should stop.
⚡ The “Define the Stop Line” Exercise
Pick one repeatable task you do each week.
1. Set the goal
Write it as if assigning it to AI.
Example: “Prepare a weekly marketing performance summary.”
2. Map the actions
What would AI need to do? Pull data. Analyze trends. Draft insights. Send report.
3. Draw the boundary
For each step, decide:
Safe to automate
Needs review
Never Automate
The point isn’t to slow AI down. It’s to decide where control lives.
Before AI executes, you define the limits. ✅
💡 Nvidia Earnings: The AI Infrastructure Moment
In a recent interview, Jensen Huang broke down Nvidia’s latest earnings, highlighting continued revenue growth driven by data center demand and AI chip adoption.
The signal is clear. AI isn’t slowing down. Compute demand is scaling fast, and infrastructure is becoming the real battleground.
If you want insight into where AI investment is actually flowing, this one’s worth a watch.
💡 Agentic AI In 2026: Four Predictions For Business Leaders
Agentic AI is moving from experimentation to enterprise strategy. According to Forbes, here’s what to watch:
C-suite roles will evolve as agentic AI becomes a strategic capability
Winning organizations will treat agentic AI as core infrastructure
AI agents will function like a new class of “intellectual worker,” requiring new oversight skills
Machine identity security will become the biggest blind spot in adoption
This isn’t about smarter chat. It’s about restructuring how decisions, control, and accountability work at scale.
RAG vs Agentic AI: How LLMs Connect Data for Smarter AI
Martin Keen and Cedric Clyburn from IBM break down how Agentic AI combined with RAG is changing how large language models operate.
The shift is simple but powerful. Instead of just generating text, systems can retrieve real-time information, plan next steps, and act with more context and accuracy.
If you want to understand where LLMs are headed beyond chat, this one connects the dots. 👇
🎁 IBM’s No-Cost Learning Path: Build with Agentic AI
If you’re ready to go beyond prompts, IBM’s free learning path breaks agentic AI into clear, buildable layers:
Foundations of Agentic AI – understand how autonomous systems reason and plan
Building with CrewAI and LangChain – create structured multi-agent workflows
Custom Tools and Workflow Design – connect agents to real data and tools
Applied Multi-Agent Solutions – deploy use cases in healthcare, customer support, and document analysis
This is hands-on and practical. You’ll use tools like CrewAI, LangGraph, AutoGen, and PydanticAI to design multi-agent workflows across healthcare, customer support, and document analysis. If you want to design AI systems, not just use them, this is a strong place to start!
This week, try giving AI a goal instead of a prompt and notice where it performs well and where you need to define boundaries.
Next week, we’ll go deeper into how agents use memory and tools behind the scenes, and why that’s the next layer of real AI fluency.
Link to ➡️ Previous Volume
💛 If this helped, feel free to share it with someone learning AI. 💛
