8 pieces · 79 minutes
Agent systems
What an agent is made of, and which layer is actually deciding the outcome.
Covers: Agent systems · AI agents
OpenAI's Decisions API makes routing, scoring, and bounded choices a distinct layer in an agent system. That is useful, but only if teams treat those decisions as governed controls with evidence, thresholds, and escalation paths.
02AgenticOps Needs More Than Autonomy. It Needs Proof.Agent systems49mCisco and Omdia's new research shows why network operations is moving from advisory AI to governed action. The next step is not more autonomy for its own sake, but an evidence loop that lets every production change be explained, verified, and recovered safely.
03The More Agent Work You Run, the More Human Steering MattersAgent systems410mRunning more AI agents changes the bottleneck. The hard work moves toward task design, intervention, verification, and integration—not away from people altogether.
04Beyond the Prompt: The Agent System Stack ExplainedAgent systems912mContext, 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.
05Harness 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.
06Loop 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.
07Autonomous 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.
08AI 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.