From the Author’s Desk
Last week we looked at the moment AI stops just answering questions and starts using tools to complete tasks. But once AI can use tools, another shift happens.
The work can be split across multiple systems.
Across the industry, many AI applications are now designed so different components handle different parts of a task. Instead of one model doing everything, the system coordinates how the work gets done.
This week we’ll look at how those systems operate and why AI is starting to behave less like a chatbot and more like a workflow engine.
When AI Starts Working in Teams
When AI receives a complex goal, it rarely solves it in a single step.
Instead, the system breaks the problem into smaller tasks and coordinates how those tasks get completed.
In technical terms, this is called task decomposition, often used in multi-agent architectures. The system determines what steps are needed, retrieves information, runs tools, and combines the results into a final answer.
A simple real-life comparison is planning family dinner. You start with a goal: cook dinner. That goal naturally splits into smaller actions like checking the pantry, buying ingredients, and preparing the meal.
AI systems operate in a similar way. They take a goal, divide it into manageable steps, and coordinate the actions needed to produce the final outcome.
The Mindset Shift: Look Past the Output
Modern AI systems behave more like workflow managers.
They decide what needs to happen next, trigger tools or processes, and move information between steps until the task is complete.
The intelligence isn’t just in the answer. It’s in how the work is organized.
⚡ The “Spot the Workflow” Exercise
This week, try asking your AI tool something slightly complex:
“Research the latest Nvidia AI chip announcement and explain what it means for the industry.”
Now watch the response.The system may search the web, analyze multiple sources, summarize findings, and generate an explanation. What looks like one answer is actually a sequence of coordinated steps.
Once you notice this, you’ll start seeing AI less as a chatbot and more as a system that organizes work. ✅
💡 FedEx Is Building an AI Agent Workforce
One of the clearest signals of where AI is heading: companies are beginning to deploy teams of AI agents.
FedEx is experimenting with AI agents across major parts of its business, including logistics planning, network optimization, and marketing operations. The architecture includes specialized agents acting as managers, workers, and auditors, coordinating tasks to support human teams.
Instead of a single AI tool performing one task, these systems operate more like digital workforces. It’s an early glimpse of how businesses are moving from AI assistants to coordinated AI systems that execute real work!
💡 Nvidia Is Launching an Open Platform for AI Agents
Nvidia is reportedly developing a platform called NemoClaw, designed specifically for building and deploying AI agents.
The platform aims to help companies create systems that can triage requests, route tasks, and resolve workflows automatically across business applications.
As AI systems become more capable, the challenge is shifting from model intelligence to how multiple systems coordinate reasoning, tools, and actions.
Platforms like this could accelerate the rise of AI systems that operate less like standalone models and more like coordinated teams of digital workers!
Agentic AI in 2026: The Complete Zero-to-Hero Guide
A new LinkedIn guide on Agentic AI explains how the next wave of AI skills is shifting from using tools to building systems that complete real tasks.
Inside the guide:
• What Agentic AI actually is (beyond chatbots)
• Core capabilities: planning, tool use, decision loops
• Skills to design AI workflows and multi-agent systems
• Platforms to start building with little or no coding
If you’re curious how people are moving from AI user → AI builder, this guide is a great starting point. 👇
AI Teamwork: How Agents and LLMs Collaborate
In this short explainer, IBM’s Anna Gutowska breaks down how multi-agent systems allow multiple AI agents to collaborate, share information, and coordinate tasks to solve complex problems.
Instead of one model doing everything, teams of agents can learn, adapt, and scale their capabilities together, making AI systems far more powerful.
A great visual walkthrough if you want to see how AI collaboration actually works behind the scenes.
This week, notice when AI searches, analyzes, or takes a moment before answering. That’s the system coordinating the work.
Next week, we’ll explore how AI systems remember information across interactions and why memory is becoming a critical layer of modern AI.
Link to ➡️ Previous Volume
💛 If this helped, feel free to share it with someone learning AI. 💛
