AI Agents vs. Agentic AI, The Emerging Venture Archetypes, and Doing More vs. Doing What Matters

Set the standards.

Good morning

In today's edition, among other things:

• Why Most "AI Agents" Aren't Actually Agents (AI Agents vs Agentic AI)

• The Emerging Venture Archetypes

• Doing More vs. Doing What Matters

• Top Tech Hubs in Europe

• State of Talent 2025

• The Stack of Choice for AI Agentic Tools

• How Spotify Is Positioning Its Teams For The Al Era

• How to Manage Startup Equity

Onwards!

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Why Most "AI Agents" Aren't Actually Agents (AI Agents vs Agentic AI)

The LinkedIn world has gone agent-crazy. Every startup now claims their chatbot is an "agent," while Big 4 consultants breathlessly pitch "agentic AI" as the next paradigm shift.

Companies are building elaborate multi-agent orchestrations for problems a simple tool-augmented LLM could solve, while others cripple genuinely complex workflows with single-agent architectures.

Real AI agents possess three qualities that most current systems fake: genuine autonomy, reactive adaptation, and task persistence. Your customer service "agent" that needs constant prompt engineering? That's automation theater, not agency.

• OpenAI Operator or Anthropic's Computer Use project, where GPT or Claude navigates actual computer interfaces to complete open-ended tasks. That's closer to true agency—the system perceives, decides, and acts with minimal human scaffolding. Compare that to most enterprise "agents" that break the moment their training data doesn't perfectly match the input.

• The tell is in the failure modes. True agents degrade gracefully when encountering novel situations. Fake agents crash spectacularly or produce confidently wrong outputs.

What Exactly Is an AI Agent?

Let’s get sharp on the terminology. An AI Agent is an autonomous software entity built to perform goal-directed tasks in a bounded digital environment. Three traits define it:

• Autonomy: It can act with little to no human intervention after setup, processing inputs, making decisions, and executing actions at scale (think customer support chatbots that resolve tickets, or scheduling assistants that find the best meeting time across calendars).

• Task-Specificity: Agents excel at narrowly scoped work. They are not generalists—they’re the calculator, not the mathematician.

• Reactivity: They respond to real-time changes—API calls, user inputs, shifting data—but their adaptation is limited. Feedback loops help, but don’t expect them to reflect deeply or remember long-term history without explicit design.

These agents are almost always built atop LLMs or LIMs, relying on their capacity for language, pattern recognition, and some basic forms of learning. But the agent is not the model—it’s after specific, automatable tasks.

AI Agents are modular, efficient, but they are not designed for complexity, ambiguity, or emergent behavior.

What happens when the job isn’t a single task, but a web of interdependent sub-tasks? When the context can change mid-flight? When the problem requires debate, negotiation, or resilience against failure?

Agentic AI is what emerges when you move from “one agent, one task” to a collaborative, coordinated ecosystem of specialized agents—each with their own perspective, tools, and sometimes even goals.

• Multi-Agent Collaboration: Instead of one “copilot,” you get a swarm—retrievers, planners, summarizers, validators—each agent handling a part of the process, often communicating asynchronously.

• Dynamic Task Decomposition: Problems are split, delegated, and re-combined on the fly. Planning agents might map out a workflow, assign roles, and monitor execution across the swarm.

• Persistent Memory and Context Sharing: Unlike stateless bots, Agentic AI systems maintain context across agents and over time. Shared memory enables agents to reference each other’s work, correct errors, or update plans as new information arrives.

• Orchestrated Autonomy: There’s often a meta-agent or orchestrator responsible for the overall strategy—resolving conflicts, assigning roles, and integrating outputs into coherent, goal-oriented results.

To understand this, it’s great to know the taxonomy here:

The key attributes: