Building Custom Agents

Build, package, and register your own AI-powered agent plugins for Harness using Go and the Drone plugin architecture.

Learn how to build, package, and register your own AI-powered agent plugins for Harness. Agent plugins follow the Drone plugin architecture — a Go-based pattern where each plugin is a Docker container that receives configuration via environment variables and executes autonomous workflows.

Go	Primary language
Drone	Plugin architecture
Docker	Containerized runtime
Harness API	Native integration

---

Plugin Architecture

Harness agent plugins follow the standard Drone plugin architecture. Each plugin is a Go binary that runs inside a Docker container. Configuration is passed via environment variables with a PLUGIN_ prefix, and the plugin implements a Plugin struct with an Exec() method.

Core Components

• CLI Framework (main.go) — Uses urfave/cli for command-line argument parsing. Defines flags that map to PLUGIN_-prefixed environment variables.

• Business Logic (plugin.go) — Contains the Plugin struct with all configuration fields and the Exec() method that implements the agent's core workflow.

• Agent Binaries (bin/) — Pre-compiled AI agent binaries (e.g., ai-code-agent, remediation-agent) that the plugin orchestrates.

• Docker Container — Multi-stage Dockerfile that builds the Go binary and packages it with runtime dependencies.

package main

import (
    "os"
    "os/exec"
    "github.com/pkg/errors"
    "github.com/sirupsen/logrus"
)

type Plugin struct {
    // Required fields
    WorkingDirectory string
    AnthropicAPIKey  string
    // Optional features
    DetailedLogging  bool
    // Harness API integration
    HarnessAPIKey      string
    HarnessAccountID   string
    HarnessOrgID       string
    HarnessProjectID   string
    HarnessPipelineID  string
    HarnessExecutionID string
    HarnessBaseURL     string
}

func (p *Plugin) Exec() error {
    // 1. Validate configuration
    if p.WorkingDirectory == "" {
        return errors.New("working directory is required")
    }
    if p.AnthropicAPIKey == "" {
        return errors.New("Anthropic API key is required")
    }

    // 2. Setup logging
    if p.DetailedLogging {
        logrus.SetLevel(logrus.DebugLevel)
    }

    // 3. Execute agent binary
    cmd := exec.Command("/root/bin/ai-code-agent",
        "--working-dir", p.WorkingDirectory,
    )
    cmd.Dir = p.WorkingDirectory
    cmd.Stdout = os.Stdout
    cmd.Stderr = os.Stderr
    cmd.Env = append(os.Environ(),
        "ANTHROPIC_API_KEY="+p.AnthropicAPIKey,
    )

    if err := cmd.Run(); err != nil {
        return errors.Wrap(err, "agent execution failed")
    }
    return nil
}

DRONE PLUGIN PATTERN

If you've built Drone plugins before, the same patterns apply to agent plugins.

---

Project Structure

Each agent plugin follows a standard directory layout:

my-agent-plugin/
├── main.go              # CLI entry point with flag definitions
├── plugin.go            # Plugin struct and Exec() business logic
├── go.mod               # Go module definition
├── go.sum               # Dependency checksums
├── Dockerfile           # Multi-stage Docker build
├── Makefile             # Build automation targets
├── README.md            # Plugin documentation
└── bin/                 # Pre-compiled agent binaries
    └── ai-code-agent    # AI coding agent binary

File	Purpose
main.go	CLI entry point. Defines flags, maps to PLUGIN_ env vars, constructs Plugin struct, and calls Exec()
plugin.go	Core business logic. Contains Plugin struct with configuration fields and the Exec() method
go.mod	Go module definition with required dependencies (urfave/cli, logrus, godotenv, pkg/errors)
Dockerfile	Multi-stage Docker build: compile Go binary, then copy to slim runtime image with agent binaries
Makefile	Build targets: build (local binary), build-docker (Docker image), push (Docker Hub)
bin/	Pre-compiled agent binaries that the plugin orchestrates at runtime

package main

import (
    "os"
    "github.com/joho/godotenv"
    "github.com/sirupsen/logrus"
    "github.com/urfave/cli"
)

func main() {
    app := cli.NewApp()
    app.Name = "my-agent-plugin"
    app.Usage = "Harness AI Agent Plugin"
    app.Action = run
    app.Flags = []cli.Flag{
        cli.StringFlag{
            Name:   "working-directory",
            Usage:  "path to the git repository",
            EnvVar: "PLUGIN_WORKING_DIRECTORY",
        },
        cli.StringFlag{
            Name:   "anthropic-api-key",
            Usage:  "Anthropic API key for Claude",
            EnvVar: "PLUGIN_ANTHROPIC_API_KEY,ANTHROPIC_API_KEY",
        },
        cli.BoolFlag{
            Name:   "detailed-logging",
            Usage:  "enable debug-level logging",
            EnvVar: "PLUGIN_DETAILED_LOGGING",
        },
        cli.StringFlag{
            Name:   "prompt",
            Usage:  "task prompt for the agent",
            EnvVar: "PLUGIN_PROMPT",
        },
    }
    if err := app.Run(os.Args); err != nil {
        logrus.Fatal(err)
    }
}

func run(c *cli.Context) error {
    if env := c.String("env-file"); env != "" {
        godotenv.Load(env)
    }
    plugin := Plugin{
        WorkingDirectory: c.String("working-directory"),
        AnthropicAPIKey:  c.String("anthropic-api-key"),
        DetailedLogging:  c.Bool("detailed-logging"),
    }
    return plugin.Exec()
}

---

Building the Plugin

Required Dependencies

module my-agent-plugin

go 1.24

require (
    github.com/joho/godotenv v1.5.1
    github.com/pkg/errors v0.9.1
    github.com/sirupsen/logrus v1.9.3
    github.com/urfave/cli v1.22.14
)

Environment Variable Mapping

Each CLI flag maps to a PLUGIN_-prefixed environment variable. Harness also auto-populates platform context variables at runtime.

CLI Flag	Environment Variable	Description
--working-directory	PLUGIN_WORKING_DIRECTORY	Path to the cloned git repository
--anthropic-api-key	PLUGIN_ANTHROPIC_API_KEY	Anthropic API key for Claude AI
--detailed-logging	PLUGIN_DETAILED_LOGGING	Enable debug-level logging
--prompt	PLUGIN_PROMPT	Task prompt for the agent
(auto-populated)	HARNESS_ACCOUNT_ID	Harness account identifier
(auto-populated)	HARNESS_ORG_ID	Harness organization identifier
(auto-populated)	HARNESS_PROJECT_ID	Harness project identifier
(auto-populated)	HARNESS_EXECUTION_ID	Pipeline execution identifier

AUTO-POPULATED VARIABLES

Harness automatically injects HARNESS_ACCOUNT_ID, HARNESS_ORG_ID, HARNESS_PROJECT_ID, and HARNESS_EXECUTION_ID when running plugins in CI pipelines — no manual configuration needed.

Build with Makefile

PLUGIN_NAME := my-agent-plugin
DOCKER_REPO := yourdockerhub/$(PLUGIN_NAME)

.PHONY: build build-docker push clean

build:
	CGO_ENABLED=0 GOOS=linux GOARCH=arm64 \
		go build -o $(PLUGIN_NAME) .

build-docker:
	docker build -t $(DOCKER_REPO):latest .

push:
	docker push $(DOCKER_REPO):latest

clean:
	rm -f $(PLUGIN_NAME)

---

Docker Packaging

Agent plugins are packaged as Docker containers using a multi-stage build. The first stage compiles the Go binary; the second creates a minimal runtime image.

# Stage 1: Build the Go plugin binary <a href="#stage-1-build-the-go-plugin-binary" id="stage-1-build-the-go-plugin-binary"></a>
FROM golang:1.24 AS builder
WORKDIR /app
COPY go.mod go.sum* ./
RUN go mod download
COPY . .
RUN CGO_ENABLED=0 GOOS=linux GOARCH=arm64 \
    go build -o my-agent-plugin .

# Stage 2: Create minimal runtime image <a href="#stage-2-create-minimal-runtime-image" id="stage-2-create-minimal-runtime-image"></a>
FROM --platform=linux/arm64 debian:bookworm-slim

RUN apt-get update && \
    apt-get install -y --no-install-recommends \
    ca-certificates git bash && \
    rm -rf /var/lib/apt/lists/*

WORKDIR /root/
COPY --from=builder /app/my-agent-plugin .
COPY bin/* ./bin/
RUN chmod +x ./my-agent-plugin ./bin/*

ENTRYPOINT ["/root/my-agent-plugin"]

Runtime dependencies: ca-certificates for HTTPS API calls, git for repository operations, bash for shell script execution.

Image size notes: Agent binaries are ~24–27 MB each. Use debian:bookworm-slim as the base and CGO_ENABLED=0 for a statically-linked Go binary.

MULTI-STAGE BUILDS

Always use multi-stage Docker builds. Never include the Go toolchain, source code, or build artifacts in the runtime image.

---

Harness API Integration

Agent plugins can integrate with the Harness API to fetch pipeline execution data and retrieve logs from failed steps.

# Fetch pipeline execution details (includes step graph) <a href="#fetch-pipeline-execution-details-includes-step-graph" id="fetch-pipeline-execution-details-includes-step-graph"></a>
GET /pipeline/api/pipelines/execution/{executionId}
    ?accountIdentifier={accountId}
    &orgIdentifier={orgId}
    &projectIdentifier={projectId}

# Download step logs from the log service <a href="#download-step-logs-from-the-log-service" id="download-step-logs-from-the-log-service"></a>
POST /log-service/blob/download
    ?accountID={accountId}
    &prefix={logBaseKey}

Failed step detection: traverse the execution graph, look for steps with status Failed or IgnoreFailed, validate failureInfo for error messages, then extract the logBaseKey field for log retrieval.

func (p *Plugin) fetchFailedStepLogs() (string, error) {
    url := fmt.Sprintf(
        "%s/pipeline/api/pipelines/execution/%s"+
        "?accountIdentifier=%s&orgIdentifier=%s&projectIdentifier=%s",
        p.HarnessBaseURL,
        p.HarnessExecutionID,
        p.HarnessAccountID,
        p.HarnessOrgID,
        p.HarnessProjectID,
    )
    req, _ := http.NewRequest("GET", url, nil)
    req.Header.Set("x-api-key", p.HarnessAPIKey)
    resp, err := http.DefaultClient.Do(req)
    if err != nil {
        return "", errors.Wrap(err, "failed to fetch execution")
    }
    defer resp.Body.Close()
    // Parse response, find failed steps, extract logBaseKey, download logs...
    return logs, nil
}

---

Template Registration

Once your Docker image is built and pushed, register it as a Harness Agent by creating a template in the agents repository.

Step 1: Create Template Directory

templates/my-custom-agent/
├── metadata.json    # Required: name, description, version
├── pipeline.yaml    # Required: pipeline definition using your plugin
├── wiki.MD          # Optional: user-facing documentation
└── logo.svg         # Optional: icon for the Harness UI

Step 2: Define metadata.json

{
  "name": "my custom agent",
  "description": "Automatically performs custom analysis and code modifications",
  "version": "1.0.0"
}

Step 3: Define pipeline.yaml

pipeline:
  clone:
    depth: 50
  stages:
    - name: my-agent
      steps:
        - name: run-agent
          run:
            container:
              image: yourdockerhub/my-agent-plugin:latest
            with:
              working_directory: /harness
              detailed_logging: "true"
            env:
              ANTHROPIC_API_KEY: <+inputs.anthropicKey>
              HARNESS_KEY: <+inputs.harnessKey>
        - name: show-diff
          run:
            shell: bash
            script: |
              git diff
        - name: create-pr
          run:
            container:
              image: himanshu6956/create-pr-plugin:latest
            env:
              HARNESS_KEY: <+inputs.harnessKey>
      platform:
        os: linux
        arch: arm64
  inputs:
    anthropicKey:
      type: secret
      description: Anthropic API key for Claude AI
    harnessKey:
      type: secret
      description: Harness API key for platform operations
    repo:
      type: string
      description: Target repository name
    branch:
      type: string
      description: Target branch
      default: main

Step 4: Write wiki.MD

# My Custom Agent <a href="#my-custom-agent" id="my-custom-agent"></a>

## Overview <a href="#overview" id="overview"></a>
This agent automatically analyzes your codebase and applies
intelligent modifications using Claude AI.

## Inputs <a href="#inputs" id="inputs"></a>
| Input | Type | Required | Description |
|-------|------|----------|-------------|
| anthropicKey | secret | Yes | Anthropic API key |
| harnessKey | secret | Yes | Harness API key |
| repo | string | Yes | Repository name |
| branch | string | No | Target branch (default: main) |

## Troubleshooting <a href="#troubleshooting" id="troubleshooting"></a>
- Ensure your Anthropic API key has sufficient credits
- Verify the Harness API key has project-level permissions

Metadata Validation Rules

• Directory names: lowercase with hyphens (e.g., my-custom-agent)

• Metadata name: lowercase with spaces (e.g., "my custom agent")

• Input names in pipeline.yaml: camelCase (e.g., anthropicKey)

• Version: semantic versioning (e.g., "1.0.0")

AUTOMATED REVIEW

Submit your template as a pull request to the agents repository. GitHub Actions runs automated Claude Code review to validate naming conventions, security rules, and cross-file consistency.

---

Testing & Deployment

Local Testing

# Build the plugin binary <a href="#build-the-plugin-binary" id="build-the-plugin-binary"></a>
cd my-agent-plugin
make build

# Set required environment variables <a href="#set-required-environment-variables" id="set-required-environment-variables"></a>
export PLUGIN_WORKING_DIRECTORY="/path/to/test-repo"
export ANTHROPIC_API_KEY="sk-ant-xxxxx"
export PLUGIN_DETAILED_LOGGING="true"

# Run the plugin locally <a href="#run-the-plugin-locally" id="run-the-plugin-locally"></a>
./my-agent-plugin

Docker Testing

# Build Docker image <a href="#build-docker-image" id="build-docker-image"></a>
make build-docker

# Run in Docker with mounted test repo <a href="#run-in-docker-with-mounted-test-repo" id="run-in-docker-with-mounted-test-repo"></a>
docker run --rm \
  -v /path/to/test-repo:/workspace \
  -e PLUGIN_WORKING_DIRECTORY=/workspace \
  -e ANTHROPIC_API_KEY=sk-ant-xxxxx \
  yourdockerhub/my-agent-plugin:latest

Pipeline Testing

pipeline:
  clone:
    depth: 50
  stages:
    - name: test-agent
      steps:
        - name: run-agent
          run:
            container:
              image: yourdockerhub/my-agent-plugin:latest
            with:
              working_directory: /harness
              detailed_logging: "true"
            env:
              ANTHROPIC_API_KEY: <+secrets.getValue("anthropic_api_key")>
      platform:
        os: linux
        arch: arm64

Deployment Checklist

• Plugin binary builds without errors

• Docker image builds and runs successfully

• All required inputs are validated in Exec()

• Sensitive values (API keys, tokens) are never logged

• Agent works correctly in a Harness CI pipeline

• Template passes metadata.json and pipeline.yaml validation

• wiki.MD provides clear documentation

SECRET MASKING

Always mask sensitive values in plugin logging. Never log the full value of secrets, tokens, or API keys.

---

Plugin Composition Patterns

Agents can be composed from multiple plugins in a single pipeline.

Pattern 1: Two-Stage Analysis + Fix

steps:
  - name: analyze-failure
    run:
      container:
        image: yourdockerhub/remediation-agent:latest
      with:
        working_directory: /harness
      env:
        ANTHROPIC_API_KEY: <+inputs.anthropicKey>
        HARNESS_KEY: <+inputs.harnessKey>
  - name: apply-fix
    run:
      container:
        image: yourdockerhub/coding-agent:latest
      with:
        working_directory: /harness
        # Automatically discovers task.txt from step 1
      env:
        ANTHROPIC_API_KEY: <+inputs.anthropicKey>
  - name: create-pr
    run:
      container:
        image: himanshu6956/create-pr-plugin:latest
      env:
        HARNESS_KEY: <+inputs.harnessKey>

Pattern 2: Standalone with Custom Prompt

steps:
  - name: run-agent
    run:
      container:
        image: yourdockerhub/coding-agent:latest
      with:
        working_directory: /harness
        prompt: <+inputs.prompt>
        max_iterations: "300"
      env:
        ANTHROPIC_API_KEY: <+inputs.anthropicKey>

Pattern 3: Multi-Model Pipeline

steps:
  # Fast analysis with Claude Sonnet
  - name: analyze
    run:
      container:
        image: agent-container:latest
      env:
        MODEL: claude-sonnet-4-5-20250929
        ANTHROPIC_API_KEY: <+inputs.anthropicKey>
      shell: bash
      script: |
        # Quick analysis of repo structure

  # Detailed generation with Claude Opus
  - name: generate
    run:
      container:
        image: agent-container:latest
      env:
        MODEL: claude-opus-4-6
        ANTHROPIC_API_KEY: <+inputs.anthropicKey>
      shell: bash
      script: |
        # High-quality code generation

Shared Containers

Container	Purpose	Used By
anewdocker25/mydockerhub:coding-agent	AI-powered code modification	Autofix, Code Review, Feature Flag Cleanup, React Upgrade
anewdocker25/mydockerhub:remediation-agent	Error analysis and diagnosis	Autofix, Manifest Remediator
himanshu6956/create-pr-plugin:latest	Multi-SCM pull request creation	Autofix, Code Coverage, React Upgrade

REUSABLE BUILDING BLOCKS

The coding-agent and create-pr-plugin containers are designed to be composed together in custom agent pipelines — you don't need to build them from scratch.