# Example 01: Agent Runtime

**Capability:** Claude Code executes tools autonomously, streams output as it works,
and loops until the task is complete. No user input needed between steps.

**OpenClaw equivalent:** Long-running daemon with tool execution and streaming output.

---

## The Prompt

```
Research the top 3 AI frameworks released this month, compare their GitHub stars,
and write a summary to research-output.md

For each framework include:
- Name and release date
- GitHub URL and current star count
- One sentence on what problem it solves
- Your verdict on whether it's worth watching

End the file with a "Verdict" section that ranks all three.
```

---

## What Happens

Claude Code will:

1. Use WebSearch to find AI framework releases from the current month
2. Use WebFetch to retrieve GitHub pages and extract star counts
3. Loop through each framework, gathering data tool call by tool call
4. Use Write to create `research-output.md` with the structured summary
5. Report completion with a summary of what was written

You will see each tool call streamed as it happens. Claude does not ask for
confirmation between steps unless it hits something ambiguous.

---

## Expected Output

After 30-60 seconds, you should see a new file `research-output.md` in the
project root. It will look something like this (content varies by month):

```markdown
# AI Frameworks Released This Month

## 1. ExampleFramework
- **Released:** March 12, 2026
- **GitHub:** https://github.com/example/framework (4,200 stars)
- **What it solves:** Simplifies multi-agent orchestration for Python developers.
- **Verdict:** Worth watching. Growing fast with strong community momentum.

## 2. ...

## Verdict
1. ExampleFramework - most practical for production use
2. ...
```

**How you know it worked:**
- A file called `research-output.md` exists in the project root
- It contains 3 frameworks with star counts and URLs
- It ends with a ranked Verdict section
- You saw WebSearch and WebFetch tool calls streaming in the terminal

---

## Why This Matters

This is the agent loop in action: plan, execute, observe, repeat. The same
loop that runs a 300-step pipeline also runs this 5-step research task.
The difference is only scale.

Claude Code v2.1.84 added adaptive thinking, which adjusts reasoning depth
automatically. Complex sub-tasks get more thought; simple ones proceed fast.

---

## Carry Forward

You just built `research-output.md`. Hold onto it.

- **Example 02** organizes raw output like this into a structured project
- **Example 06** sends research through a multi-agent review cycle
- **Example 10** uses this exact capability as step 1 of a full pipeline

Every example builds on the same core loop: Claude plans, executes tools, observes results, and repeats until done. The task changes. The pattern does not.

---

## Now Try It Yourself

The demo researched AI frameworks. Replace the topic with something you actually need:

```
Research the top 3 [your topic] this [time period]. For each, include:
- [what you need to know]
- [specific data points: URLs, numbers, dates]
- Your verdict on [your evaluation criteria]

Write the summary to research-output.md with a ranked Verdict section at the end.
```

**The pattern you just learned:** research question + output structure + file destination. Claude handles everything between the question and the written answer.

Ideas worth trying:
- Competitors in your market and their latest announcements
- Tools for a workflow you want to improve
- Developments in a technology you are evaluating for work