How to build autonomous coding agents that actually work

Most AI coding tools just autocomplete your next line. But what if you could build agents that actually understand your project, make complex decisions, and execute multi-step coding tasks without constant supervision?

That's where autonomous coding agents shine. Unlike traditional AI assistants that need you to hold their hand through every step, these agents can plan, execute, and iterate on coding tasks independently. They're like having a junior developer who never gets tired and can work on multiple branches simultaneously.

What are autonomous coding agents?

Agents are specialized AI assistants that can be configured for specific tasks and workflows. They allow you to create focused tools with custom prompts, models, and tool access. Unlike simple chatbots, autonomous coding agents can:

• Make decisions about which files to edit based on project structure
• Execute shell commands and handle errors independently
• Iterate on solutions until they pass tests
• Switch between different specialized modes (planning, building, debugging)
• Maintain context across long coding sessions

The key design principle behind Claude Code is that Claude needs the same tools that programmers use every day. It needs to be able to find appropriate files in a codebase, write and edit files, lint the code, run it, debug, edit, and sometimes take these actions iteratively until the code succeeds. We found that by giving Claude access to the user's computer (via the terminal), it had what it needed to write code like programmers do.

Why OpenCode makes the perfect foundation?

OpenCode stands out because it's built specifically for autonomous operation. OpenCode is an open source agent that helps you write code in your terminal, IDE, or desktop. With over 100,000 GitHub stars, 700 contributors, and over 9,000 commits, OpenCode is used and trusted by over 2.5M developers every month.

What makes OpenCode special for autonomous workflows:

• Built-in agent system: Build is the default primary agent with all tools enabled. This is the standard agent for development work where you need full access to file operations and system commands.
• Persistent sessions: Unlike IDE plugins that lose context when you close them, OpenCode maintains conversation state
• Multiple agent types: Switch between planning, building, and reviewing modes
• Skills system: Agent skills let OpenCode discover reusable instructions from your repo or home directory. Skills are loaded on-demand via the native skill tool—agents see available skills and can load the full content when needed.

How to set up OpenCode for autonomous workflows?

First, install OpenCode using one of these methods:

# Quick install
curl -fsSL https://opencode.ai/install | bash

# Package managers
npm i -g opencode-ai@latest
brew install anomalyco/tap/opencode

After installation, you'll need to configure your AI provider. Run OpenCode and use the /connect command to set up your API keys. Run the /connect command in the TUI, select opencode, and head to opencode.ai/auth. Sign in, add your billing details, and copy your API key.

For autonomous agents, I recommend using Claude models because they excel at sustained reasoning and planning. You can configure this in your opencode.json:

{
  "agent": {
    "build": {
      "model": "anthropic/claude-sonnet-4-20250514",
      "tools": {
        "write": true,
        "edit": true,
        "bash": true
      }
    },
    "plan": {
      "model": "anthropic/claude-haiku-4-20250514",
      "tools": {
        "write": false,
        "edit": false,
        "bash": false
      }
    }
  }
}

What skills should you teach your agent?

Create one folder per skill name and put a SKILL.md inside it. OpenCode searches these locations: For project-local paths, OpenCode walks up from your current working directory until it reaches the git worktree. It loads any matching skills//SKILL.md in .opencode/ and any matching .claude/skills//SKILL.md or .agents/skills/*/SKILL.md along the way.

Here's how to create a custom skill for HTML benchmarking (like in the video example):

# Create skills directory
mkdir -p .opencode/skills/html-benchmark

# Create the skill file
touch .opencode/skills/html-benchmark/SKILL.md

Inside your SKILL.md:

---
description: "Run HTML benchmarks across multiple AI models"
---

# HTML Benchmark Testing Skill

This skill runs HTML file comparisons across different AI models to evaluate output quality.

## Workflow:
1. Create test HTML files for each model
2. Run them in parallel using grid-style layout
3. Compare visual output and functionality
4. Generate comparison report

## Usage:
- Use this when testing AI-generated HTML across models
- Helpful for evaluating design consistency
- Good for A/B testing different prompting approaches

## Commands:
```bash
# Create grid layout for comparison
open -a "Google Chrome" file1.html file2.html file3.html

# Run in parallel
for file in *.html; do open "$file" & done

## How to build multi-step autonomous workflows?

The key to successful autonomous agents is giving them clear, structured workflows they can follow. Here's a pattern that works well:

### Planning Phase
Start every complex task with the planning agent: A restricted agent designed for planning and analysis. We use a permission system to give you more control and prevent unintended changes.

```bash
# Switch to planning mode
# Press Tab key or use configured switch_agent keybind

# Give it a complex task
"Create a React component that fetches data from an API, handles loading states, and displays results in a table with sorting"

The planning agent will break this down into steps without making any changes to your codebase.

Execution Phase

Once you feel comfortable with the plan, switch back to Build mode by hitting the Tab key again. And asking it to make the changes.

The build agent can then execute each step:

1. Create component file structure
2. Set up API integration
3. Implement loading states
4. Build table with sorting
5. Add error handling
6. Write tests

Iteration and Validation

You can undo the changes using the /undo command. OpenCode will now revert the changes you made and show your original message again.

This lets you quickly iterate on solutions that don't work perfectly the first time.

What makes Claude especially good for autonomous coding?

Claude Code is an agentic tool where developers work with Claude directly from their terminal—delegating tasks from code migrations to bug fixes. "Early testing shows Claude Opus 4.6 delivering on the complex, multi-step coding work developers face every day—especially agentic workflows that demand planning and tool calling. This starts unlocking long horizon tasks at the frontier." "Claude Opus 4.6 is the strongest model Anthropic has shipped. It takes complicated requests and actually follows through; breaking them into concrete steps, executing, and producing polished work even when the task is ambitious.

Claude's strengths for autonomous coding:

• Long-term reasoning: Can maintain context across complex, multi-step tasks
• Tool use: Excellent at deciding when and how to use different development tools
• Error recovery: When something breaks, Claude can debug and fix issues independently
• Planning ability: I'm more comfortable giving it a sequence of tasks across the stack and letting it run. It's smart enough to use subagents for the individual pieces.

How to create parallel execution workflows?

One of the most powerful features is running multiple agents in parallel. Spawn multiple Claude Code agents that work on different parts of a task simultaneously. A lead agent coordinates the work, assigns subtasks, and merges results.

Here's how to set up parallel HTML benchmarking like in the video:

1. Create the main coordination script:

# benchmark_coordinator.py
import subprocess
import threading

models = ["gpt-4", "claude-sonnet", "gemini-pro"]
html_tasks = ["landing-page", "dashboard", "form-validation"]

def run_model_benchmark(model, task):
    cmd = f"opencode --agent build -p 'Create {task} HTML for {model}'"
    result = subprocess.run(cmd, shell=True, capture_output=True)
    return result

# Run in parallel
threads = []
for model in models:
    for task in html_tasks:
        thread = threading.Thread(target=run_model_benchmark, args=(model, task))
        threads.append(thread)
        thread.start()

# Wait for completion
for thread in threads:
    thread.join()

2. Set up the comparison viewer:

# Create grid layout HTML
opencode -p "Create an HTML grid that displays all benchmark results side by side for easy comparison"

3. Automate the sharing: The conversations that you have with OpenCode can be shared with your team. This will create a link to the current conversation and copy it to your clipboard.

What are the common pitfalls to avoid?

Giving too much autonomy too quickly: Start with supervised runs and gradually increase automation as you build trust.

Not setting clear boundaries: We use a permission system to give you more control and prevent unintended changes. By default, all of the following are set to ask Use OpenCode's permission system to control what agents can modify.

Ignoring context limits: Since the conversation history can grow quickly and fill up the model's context, Opencode automatically summarizes the session if tokens > Math.max((model.info.limit.context - outputLimit) * 0.9, 0). Long autonomous sessions need proper context management.

Not leveraging the skill system: Create reusable skills for common patterns in your projects. This makes agents more consistent and reliable.

How to scale autonomous workflows for teams?

For team environments, consider using frameworks like OpenAgentsControl which adds structured patterns on top of OpenCode.

Store team patterns in .opencode/context/project/. Commit to repo. Everyone uses same standards. This ensures all team members' agents follow the same coding patterns and conventions.

Set up shared skills directories:

# Team skills in repo
.opencode/
  context/
    project/
      team-standards.md
      api-patterns.md
      testing-guidelines.md
  skills/
    code-review/
    deployment/
    testing/

The future of development isn't about replacing programmers—it's about amplifying what we can build. Autonomous coding agents let you focus on architecture and creativity while handling the repetitive implementation work. Start small, build trust, and gradually expand what your agents can handle independently.

Remember: As Kent Beck said after 52 years of coding, 90% of traditional programming skills are becoming commoditized while the remaining 10% becomes worth 1000x more. The developers and teams who understand this shift—who learn to orchestrate AI rather than just code alongside it—will thrive in this new landscape.