Agents Are Not Magic. They Are Processes.

Half the industry is selling agents. Half the industry is scared of them. Both camps often miss the simplest thing. An agent is a process.

The business problem is real. Founders spend on vendors that promise autonomous workflows and get brittle demos. Engineers spend weeks on frameworks when a Python script would have done the job. Cutting through the hype saves months.

The first principle

An agent is four things. A goal. A plan. A set of tools. A stop condition. That is it. Strip away the marketing and you are left with a loop.

def agent(goal, tools, max_steps=10):
    plan = llm_plan(goal)
    for step in range(max_steps):
        action = llm_next_action(goal, plan, history)
        if action.type == 'done':
            return action.result
        result = tools[action.tool](action.args)
        history.append((action, result))
    return 'stopped: max_steps'

That is the whole pattern. LangGraph, CrewAI, and the others are variations on this.

Where it gets real

Real-world agents fail in three places. Tool reliability. Stop conditions. Error recovery. If your tool times out, your agent hangs. If your stop condition is fuzzy, your agent loops. If one tool raises, the whole run is lost.

How I ship them

1. Start with a single tool.
2. Run in batch mode, not streaming.
3. Log every step.
4. Cap iterations hard.

Read the Anthropic agents primer for the best short explanation I have found.

The reframe

Stop asking if an agent can do the work. Ask what process you already run in your head. Then write that process as code and give it tools. That is agentic development. No magic. Just loops with taste.