Writings | Nick Norman

🧠 The Hidden First Step in AI Adoption: Alignment, Then Domain Focus

This post highlights why AI success starts with alignment, not algorithms. Drawing from industry insights and personal experience, it urges leaders to invest in shared understanding before deploying tech.

🤝 When AI Leaves Communities Behind: What We Miss and Why It Matters

This blog post highlights how AI efforts often overlook real community input. It calls for moving beyond symbolic gestures to meaningful participation—where communities help shape decisions, not just respond to them.

🤝 Community First: Rethinking Volunteer Onboarding in AI Spaces

This blog post highlights why thoughtful onboarding matters in early-stage AI communities. It shows how simplicity, human connection, and investing in a small core group can lead to stronger, more sustainable growth.

Don’t Turn Back at the Edge (a Word of Encouragement for Agent Builders)

This post offers encouragement to those building multi-agent systems—reminding developers that real breakthroughs often happen at the edge of complexity, and that persistence, imagination, and grounded design are what turn agents into tools that truly augment the real world.

🧵When Intuitive Design Pushes Back: Early Lessons from a Google Drive–Triggered Multi-Agent System

This post shares a real-world challenge in building a lightweight agent system across platforms—highlighting how clean handoffs, not full unification, can stabilize early builds and why recognizing abstract constraints is key to sustainable multi-agent architecture.

🧠 When Intuitive Design Pushes Back: Early Lessons from a Google Drive–Triggered Multi-Agent System

🧵Scoping Cross-Continent Multi-Agent Systems

This post kicks off a blog series on how to scope multi-agent systems before building. It focuses on early planning decisions—like where agents live, how they communicate, and what compliance rules apply when building agentic systems across continents.

🧠 No Detergent, Blue Lips: The Cost of Skipping Context Engineering

This post introduces the concept of context engineering—the practice of designing agents with the memory, awareness, and structure they need to act intelligently, stay on track, and hand off work in complex environments.

🧠 Model Context Protocol Explained in Plain Language: What It is, Why It Matters, and How It Changes AI Use

This post breaks down Model Context Protocol (MCP) into simple terms, showing how it organizes tools, memory, and context across AI and multi-agent systems—so agents can work together smoothly, without repeating steps or losing track.

🧠 Rethinking Citation Workflows with Multi-Agent Systems and Interleaved Thinking

This post explores how multi-agent systems can handle messy citation lists and unpredictable links—using interleaved thinking to pause, adapt, and extract insights from complex or inaccessible sources.

Understanding ChatGPT: Why the "Why" Matters More Than the "How"

This post encourages researchers to shift focus from simply learning how to use ChatGPT to first understanding why it works—laying the foundation for more thoughtful, effective use in academic and research settings.

🧠 When Agents Think Together: Lessons from Zombies, Fireflies, and RoboBees

This post examines how diverse, reactive agents—like robotic bees or fireflies—can form powerful, decentralized swarms, offering lessons for building more resilient multi-agent systems.

🧠 Building Multi-Agent Systems that Can Disagree, Resolve Conflict, and Survive

This post explores how disagreement in multi-agent systems can be a strength, not a flaw—showing how agents resolve conflict through reasoning, reflex, or swarm behavior, and why durable systems must be built to withstand misalignment.

Emergent Behavior in Artificial Organizations: Can We Predict It?

This post unpacks how agent interactions can lead to unexpected, emergent behavior within multi-agent systems—highlighting how even simple rules can produce complex dynamics, and why developers must design with uncertainty, not just control.

The Multi-Agent System Roundtable: When Your Smartest Agents Only Speak

This post traces the evolution of conversation-only multi-agent systems—where agents don’t act, but think and speak—revealing how dialogue itself can surface strategic insight without needing execution or control.

🧠 Exploring Generative AI Through Amazon Bedrock: A Multi-Agent System Builder’s Perspective

This post offers a hands-on look at building AI agents with Amazon Bedrock—highlighting its strengths in security and simplicity, while exploring its limitations for more dynamic, collaborative multi-agent systems.

🧠 Forgetfulness as Defense: Rethinking Memory in AI Security for Multi-Agent Systems

This post examines the security risks of agent memory, arguing that in some cases—like guarding against subtle, evolving threats—stateless agents offer stronger protection by resisting influence, drift, and repeated manipulation.

It’s Not the Code, It’s the System: My Three-Phase Approach to Architecting Multi-Agent Systems

This post answers a common question—can ChatGPT write agent code?—by breaking down the real work of building multi-agent systems: moving from scaffolding to optimization to communication, where architecture, behavior, and collaboration matter far more than code alone.

🧠 Designing AI Agents for the Long Run: Avoiding the Scalability Trap

This post introduces a simple but powerful agent design framework—Go, Retrieve, Delegate—emphasizing why agents should always be built with handoff and collaboration in mind to support scalability, interoperability, and long-term system resilience.

🧠 Where Do We Start? A 5-Step Guide for Organizations Exploring AI Agents

This post offers a practical starting point for organizations exploring AI—outlining five clear steps to move from ideas to implementation, with a focus on aligning goals, minimizing friction, and building systems that create lasting value.

When Does an Agent Stop Thinking?

This post explains the difference between triggered and autonomous agents, showing how platforms like FlowFuse and PythonAnywhere work together to build real agentic systems—where agents don’t just wait for instructions, but act, adapt, and collaborate in real time.

Beyond the Browser: Understanding the Anatomy of AI Agents (at Scale)

This post breaks down the core anatomy of an AI agent—prompt, function, and control flow—to show how agents move beyond browser-based tools like ChatGPT and enable scalable, automated intelligence in real-world systems.

Strategic Allocation and Look-Ahead Thinking: Building AI Agents That Adapt to Future Challenges

This post explores how agent routing in multi-agent systems can evolve from simple task assignment to strategic, adaptive allocation—highlighting real-world use cases, dynamic reallocation, FIPA standards, and the power of look-ahead thinking in complex research environments.

Welcome to My AI Research & Exploration Blog

What sets this blog apart is clarity. I break down topics like AI strategy, community engagement, and system design in a way that’s approachable—especially for organizational leaders and teams navigating real-world adoption. (All blog posts are hosted on Hashnode.)