Andrii Bidochko

• Updated: March 23, 2026
• 4 min read

Extending the OpenClaw DevOps Agent for IaC with Terraform and Pulumi

# Extending the OpenClaw DevOps Agent for Infrastructure‑as‑Code

*Published by the UBOS Team*

—

## Introduction

Developers using the **OpenClaw DevOps Agent** often need to provision cloud resources programmatically. While OpenClaw already supports a range of CI/CD workflows, extending it to manage infrastructure as code (IaC) with tools like **Terraform** and **Pulumi** unlocks a powerful, repeatable, and version‑controlled way to spin up environments.

This guide walks you through the architecture, shows step‑by‑step code examples for both Terraform and Pulumi, and demonstrates how to integrate the agents into your existing OpenClaw pipelines.

—

## Architecture Overview

+——————-+ +——————-+ +——————-+
| OpenClaw Agent | | Terraform / | | Cloud Provider |
| (Docker/K8s) | | Pulumi CLI | | (AWS, GCP…) |
+——————-+ +——————-+ +——————-+
^ ^
| |
+— Git Repository (IaC) ——+

1. **OpenClaw Agent** runs as a Docker container (or inside a Kubernetes pod) and executes user‑defined jobs.
2. **IaC Repository** contains Terraform `.tf` files or Pulumi TypeScript/Python code.
3. The agent pulls the repo, runs the appropriate CLI, and reports the result back to OpenClaw.

—

## Prerequisites

– OpenClaw DevOps Agent up and running.
– Access token for the agent to clone private repositories.
– Terraform 1.5+ and Pulumi 3.0+ installed in the agent image (or use the official `hashicorp/terraform` and `pulumi/pulumi` images as side‑cars).
– Cloud provider credentials stored as OpenClaw secrets.

—

## Step‑by‑Step: Terraform Integration

### 1. Create an IaC Repository

hcl
# main.tf – provision an AWS S3 bucket
provider “aws” {
region = var.aws_region
}

resource “aws_s3_bucket” “example” {
bucket = “openclaw-terraform-demo-${random_id.suffix.hex}”
acl = “private”
}

resource “random_id” “suffix” {
byte_length = 4
}

### 2. Add a Job Definition in OpenClaw

yaml
jobs:
– name: terraform-apply
image: hashicorp/terraform:latest
commands:
– terraform init
– terraform apply -auto-approve
env:
AWS_ACCESS_KEY_ID: “${{ secrets.AWS_ACCESS_KEY_ID }}”
AWS_SECRET_ACCESS_KEY: “${{ secrets.AWS_SECRET_ACCESS_KEY }}”
volumes:
– “/workspace:/workspace”
workdir: /workspace

### 3. Trigger the Job

– Push a commit to the IaC repo → OpenClaw webhook starts `terraform-apply`.
– The agent runs `terraform init` and `apply` inside the container.
– On success, OpenClaw records the plan output and stores the state file as an artifact.

—

## Step‑by‑Step: Pulumi Integration

### 1. Create a Pulumi Project (TypeScript example)

typescript
import * as pulumi from “@pulumi/pulumi”;
import * as aws from “@pulumi/aws”;

const bucket = new aws.s3.Bucket(“openclaw-pulumi-demo”, {
acl: “private”,
});

export const bucketName = bucket.id;

### 2. Define an OpenClaw Job

yaml
jobs:
– name: pulumi-up
image: pulumi/pulumi:latest
commands:
– pulumi login –local
– pulumi stack init dev || true
– pulumi up -y
env:
AWS_ACCESS_KEY_ID: “${{ secrets.AWS_ACCESS_KEY_ID }}”
AWS_SECRET_ACCESS_KEY: “${{ secrets.AWS_SECRET_ACCESS_KEY }}”
volumes:
– “/workspace:/workspace”
workdir: /workspace

### 3. Run the Job

– Commit the Pulumi project → webhook triggers `pulumi-up`.
– Pulumi creates/updates the S3 bucket and prints the output URL.
– The result is captured by OpenClaw for audit and rollback.

—

## Best Practices

| Practice | Why it matters |
|———-|—————-|
| Store credentials as **OpenClaw secrets** | Keeps keys out of source control. |
| Pin Terraform/Pulumi versions in the Docker image | Guarantees reproducible builds. |
| Use remote state back‑ends (S3, GCS, Azure Blob) | Prevents state corruption when multiple agents run. |
| Tag resources with `openclaw-run-id` | Easy cleanup of temporary environments. |

—

## Conclusion

By embedding Terraform or Pulumi into the OpenClaw DevOps Agent, you gain a unified pipeline that can **build, test, and provision** infrastructure alongside application code. This approach reduces context switching, enforces version‑controlled infrastructure, and leverages OpenClaw’s powerful orchestration capabilities.

Ready to try it out? Deploy the OpenClaw Agent, add the job definitions above, and watch your IaC come to life.

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Explore the hosted OpenClaw offering to get a managed, production‑ready environment with zero‑ops scaling.

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*Happy building!*

Andrii Bidochko

CTO UBOS

Andrii Bidochko is an AI entrepreneur and researcher focused on AI agents, reinforcement learning, and autonomous systems. He writes about the technologies shaping the future of machine intelligence, from frontier models and agent architectures to real-world AI applications.