5 pieces · 52 minutes
Agent systems
What an agent is made of, and which layer is actually deciding the outcome.
Covers: Agent systems · AI agents
Context, tools, memory, harnesses, loops, and evals are different layers of an AI agent system. Here is how they fit together—and why the model alone does not decide whether an agent works.
02Harness Engineering: Why a Better Model Is Not the Whole AnswerAgent systems511mA capable model still needs a well-designed runtime around it. Harness engineering is the work of giving an agent useful context, tools, boundaries, state, and evidence of completion.
03Loop Engineering: What It Means—and Why It Is More Than Letting an Agent Run ForeverAgent systems510mLoop engineering is an emerging label for designing repeated agent work: its trigger, state, feedback, boundaries, verification, and explicit ways to stop or hand off.
04Autonomous Is Not Helpful: How Should We Evaluate Proactive AI Agents?Agent systems210mAn agent that can act without being asked is not automatically useful. The hard question is whether it noticed the right thing, had evidence, chose the right moment, and knew when to stay quiet.
05AI Agents, Explained: The Loop Behind the LabelAI agents39mA beginner-friendly map of what makes a system agentic, how the model–tool loop works, and when a simpler workflow is the better design.