🎯 Core Goals
- Define what makes an AI system truly “agentic” — not just a script with tools.
- Understand the six key characteristics of high-quality agents.
A real agent isn’t just an LLM with tools bolted on. It operates autonomously, breaks down complex goals, adapts when things go wrong, and can collaborate — like an employee who figures things out, not just a tool that waits to be used.
👁️ Visuals & Interactives
The Agent Checklist
What separates a smart script from a true AI agent?
Autonomy & Proactivity
Acts independently toward a goal — doesn't wait to be told every step. More like an employee than a tool.
Goal-Oriented Planning
Breaks high-level goals into actionable steps in a logical order — prerequisites first, then build on them. Prioritizes, sequences, and adjusts when conditions change.
Tool Use & Interoperability
Interacts with external APIs, search engines, and databases to get real things done — not just talk about them.
Adaptability
If Tool A fails, it tries Tool B. Uses real-time feedback to refine its approach mid-task.
Context & Memory
Maintains short- and long-term memory of past steps, enabling coherent work across multi-step or multi-session tasks.
Collaboration
Works alongside humans and other specialized agents to accomplish complex, multi-part projects.
Fixed steps, breaks if anything changes
Adaptive loop — keeps going until the goal is met
📝 Key Characteristics
- Autonomy & Proactivity: Unlike passive, reactive AI, a good agent acts independently to achieve goals. It doesn’t wait to be told every step — it decides what to do next.
- Goal-Oriented Planning: Agents break complex, high-level goals into actionable steps, prioritize them, and adjust strategies when conditions change.
- Tool Use & Interoperability: Good agents interact with external systems — APIs, software, search engines, databases — to get tasks done (e.g., checking email, updating a CRM).
- Adaptability & Learning: Agents use feedback loops and real-time data to refine their performance and adapt to new situations mid-task.
- Context Awareness & Memory: Agentic LLMs maintain both short-term and long-term memory of past interactions, allowing for coherent work across multiple steps or sessions.
- Collaboration: Agents can work alongside humans and often with other specialized agents to accomplish complex, multi-part projects.
A traditional script follows fixed steps: A → B → C. An agent follows an adaptive loop: Perceive → Plan → Act → Observe → Repeat. The loop continues until the goal is met — or the agent asks for help.
Remember those absurd questions, like “Peter bought 12 watermelons, Mary bought 24 apples — how many pineapples does Jack have?” A good model should respond, “No, that’s impossible.” Similarly, if you ask an impossible question, a good LLM shouldn’t say, “What a great idea!” — it should push back. Researchers have created a benchmark to test this capability: Bullshit Benchmark