Agents Working for You

What This Track Covers

10 lessons to go from "what's an agent loop?" to running autonomous agents safely and effectively.

Lesson 1

What Is an Autonomous Agent Loop?

The perceive → decide → act → repeat pattern. How agent loops differ from chat sessions. When autonomy helps and when it hurts.

Lesson 2

Sandboxing: Why and How

Why autonomous agents need isolation. Docker containers, file system restrictions, network controls, and capability gating. Setting boundaries before letting an agent run.

Lesson 3

Hermes: Setup and First Loop

Installing Hermes, configuring a model endpoint, defining tools, and running your first autonomous task.

Lesson 4

Openclaw Fleet Mode

Multi-agent orchestration with Openclaw. How fleet dispatch works, agent roles, task queues, and coordination patterns.

Lesson 5

AutoGPT: Goal Decomposition

How AutoGPT breaks a goal into sub-tasks, executes them, and iterates. Setting up AutoGPT, defining goals effectively, and understanding its limitations.

Lesson 6

Comparing the Options

Hermes vs Openclaw vs AutoGPT. Decision matrix covering setup effort, autonomy level, safety features, scalability, and use-case fit.

Lesson 7

Safety Cautions

Token burns, infinite loops, data exposure, unintended tool calls, and cost blowouts. How each risk manifests and how to guard against it.

Lesson 8

Effective Patterns

Human-in-the-loop checkpoints, logging and observability, timeout strategies, cost budgets, and escalation paths when an agent gets stuck.

Lesson 9

Building Your First Loop

Step-by-step: pick a tool, sandbox it, define a goal, set a budget, run it, monitor it, review the log. Under 45 minutes.

Lesson 10

When NOT to Use an Autonomous Agent

Recognizing tasks that need human judgment, creative decisions, or reversible steps. The autonomy continuum from chat to full loop.