Custom AI Agents

Create and update Harness AI agent instances - standalone templates used as building blocks in pipelines for automated code, agentic workflows, and infrastructure tasks.

Instructions

Follow this workflow to create or update an agent. This is INTERACTIVE — show YAML for review and wait for confirmation before creating/updating the agent.

Phase 1: Check Existing Solutions First

IMPORTANT: Before creating a new agent, check if an existing one can solve the use case.

1. List existing agents — Call harness_list with resource_type="agent" (include org_id and project_id if scoped to a project)
   • Check if any system or custom agents already exist that can handle this task
   • Ask user if they want to use/modify an existing agent instead of creating new
2. For updating existing agents — Use harness_get with resource_type="agent" and agent_id to retrieve the current agent configuration
   • Review the current spec, name, description, and other fields
   • Identify what needs to be changed (spec, name, description, wiki, logo)
   • Use harness_update (not harness_create) to update the agent with only the fields that need modification

Phase 2: Requirements Gathering

If creating a new agent or updating an existing one, collect the following before generating YAML:

1. Agent Metadata

• Name: Display name for the agent (e.g. "Code Coverage Agent", "PR Reviewer")
• Description: Brief description of the agent's purpose (optional)
• UID/UUID: Always generate this from the name and pass it explicitly to create/update APIs. Do not rely on the create API fallback.
  • Generation rule : prefix with ca_, then lowercase the name, convert spaces and hyphens (-) to _, replace any remaining non-alphanumeric runs with _, collapse duplicate _, and trim leading/trailing _ from the slug portion (e.g. "Code Coverage Agent" → ca_code_coverage_agent, "PR Reviewer" → ca_pr_reviewer)
  • If the generated UID conflicts with an existing agent, ask the user whether to reuse/update that agent or append a short suffix

2. Task Details - Interactive Requirements Gathering

Ask and clarify the following with the user:

1. Agent's exact goal: What specific outcome should the agent achieve? Be specific — avoid vague goals.
2. Inputs the agent needs: Repository info? Execution context? Configuration? Secrets?
3. Outputs the agent produces: Files? External actions? Data/metrics?
4. What the agent works on: Specific file paths? External services? Databases/APIs?
5. Task workflow: Step-by-step workflow (do 1, then 2, then 3, etc.)
6. Constraints and preferences: Limitations, rules, or coding standards (e.g., "Use idiomatic Go code", "Do not modify existing tests")
7. Definition of done: How do you know the agent succeeded? Specific criteria, artifacts, or exit conditions.

3. Recommend Configuration

Based on requirements, recommend and verify with the user:

1. Task instructions (task field):
   • Break down the goal into detailed step-by-step instructions
   • Include specific commands, file paths, and expected outcomes
   • Reference inputs using ${{inputs.fieldName}} syntax inside PLUGIN_TASK and other env vars
   • Add ## RULES section at the end with constraints formatted as markdown bullet points
2. Runtime inputs (inputs section in spec):
   • Only add if user confirms runtime parameters are needed
   • Map each input to what the agent needs (repo, branch, executionId, thresholds, etc.)
   • Always set a default value for every non-required input — if ${{inputs.fieldName}} is referenced in PLUGIN_TASK or any env var and no value is supplied at runtime nor a default exists, the agent will error at execution time
   • Always include allowedDomains so users understand and control non-LLM/non-MCP network access
3. Connectors:
   • LLM connector for model access (required for all agents) - User must create via Harness UI or MCP
   • MCP connectors for external services (GitHub, Slack, Harness platform, etc.) - only if needed
   • All authentication and secrets are managed within the connectors

Present this recommended configuration to the user and iterate until confirmed.

4. Default Configuration & Inputs

Agent Structure: Agents use agent.step.group.steps format — the run step is nested inside a named step group.

Default structure:

version: 1
agent:
  step:
    group:
      steps:
        - name: Agent
          if: <+Always>
          id: agent
          run:
            container:
              image: pkg.harness.io/vrvdt5ius7uwygso8s0bia/harness-agents/harness-ai-agent:latest
            env:
              PLUGIN_TASK: |
                <step-by-step task instructions>
              PLUGIN_MAX_TURNS: 150
              PLUGIN_HARNESS_CONNECTOR: ${{inputs.llmConnector.id}}
              PLUGIN_ALLOWED_DOMAINS: ${{inputs.allowedDomains}}

Required environment variables:

env:
  PLUGIN_TASK: |                                   # Task instructions go here as a multiline string
    <step-by-step instructions>
  PLUGIN_MAX_TURNS: 150                            # Adjust 100-200 based on task complexity
  PLUGIN_HARNESS_CONNECTOR: ${{inputs.llmConnector.id}}  # References llmConnector input's id property
  PLUGIN_ALLOWED_DOMAINS: ${{inputs.allowedDomains}}      # Regexes for additional network access

MCP configuration (only if external services needed):

env:
  PLUGIN_MCP_FORMAT: harness
  PLUGIN_MCP_SERVERS: <+connectorInputs.resolveList(<+inputs.mcpConnectors>)>  # References mcpConnectors input

Optional model override (only if user explicitly requests it):

env:
  ANTHROPIC_MODEL: ${{inputs.modelName}}  # Only add when user insists on a modelName input

Required inputs (always include):

agent:
  inputs:
    llmConnector:
      type: connector
      required: true
      default: your_llm_connector_id  # User must replace with actual connector ID
      ui:
        connectorCategories:
          - AI

    allowedDomains:
      type: string
      default: ""

Network access: Agent network access is limited to the LLM connector, configured MCP connectors, and domains matching allowedDomains. allowedDomains accepts regexes separated by |. Default to an empty string if the user does not specify domains; if they do specify domains, work with them to build the right regex.

Optional inputs (add as needed):

    # MCP connectors - only if agent needs external services
    mcpConnectors:
      type: array
      default:
        - your_github_mcp_connector  # User must replace
        - your_slack_mcp_connector   # User must replace
      ui:
        component: array
        input:
          inputType: connector
          inputConfig:
            connectorTypes:
              - Mcp
    
    # Model name override - ONLY add if user explicitly requests it
    modelName:
      type: string
      default: your_model_arn_or_id  # User must replace with their model ARN or ID
    
    # Custom parameters
    repo_name:
      type: string
      default: my-org/my-repo

layout block (always include, only list fields that are present as inputs):

The layout block controls what appears in the agent configuration UI. It contains at most four items — llmConnector, allowedDomains, modelName, and mcpConnectors — and only those that exist as first-class input fields in the inputs section. Never include any other fields (e.g. custom inputs like repo_name) in the layout block:

agent:
  layout:
    - title: Agent Configuration
      items:
        - llmConnector          # always present
        - allowedDomains        # always present
        - modelName             # only if modelName input exists
        - mcpConnectors         # only if mcpConnectors input exists

Supported input types: string, secret, boolean, connector, array

IMPORTANT: Users must create connectors via Harness UI or harness_create with resource_type="connector" before running the agent.

Phase 3: Generate Agent Spec

Assemble the complete agent YAML specification (spec field):

1. Start with version: 1 and agent: structure
2. Create agent.step.group.steps block with a single step entry:
   • name: Agent, if: <+Always>, id: agent
   • run.container.image: pkg.harness.io/vrvdt5ius7uwygso8s0bia/harness-agents/harness-ai-agent:latest
   • run.env section (all task config lives here as env vars):
     • PLUGIN_TASK: — multiline string with step-by-step instructions and ## RULES section
     • PLUGIN_MAX_TURNS: 150 (adjust 100-200 based on complexity)
     • PLUGIN_HARNESS_CONNECTOR: ${{inputs.llmConnector.id}}
     • PLUGIN_ALLOWED_DOMAINS: ${{inputs.allowedDomains}}
     • PLUGIN_MCP_FORMAT: harness (only if MCPs needed)
     • PLUGIN_MCP_SERVERS: <+connectorInputs.resolveList(<+inputs.mcpConnectors>)> (only if MCPs needed)
     • ANTHROPIC_MODEL: ${{inputs.modelName}} (only if user explicitly requests a modelName input)
3. Add agent.inputs section with:
   • llmConnector (required) with ui.connectorCategories: [AI]
   • allowedDomains (default "") to allow additional network domains using regexes
   • mcpConnectors (optional - only if needed) with ui.component: array, ui.input.inputType: connector, and ui.input.inputConfig.connectorTypes: [Mcp]
   • modelName (optional - only if user explicitly requests it)
   • Custom inputs (as needed)
4. Add agent.layout block — only include items that are present as inputs:
   • Always include llmConnector
   • Always include allowedDomains
   • Include modelName only if that input exists
   • Include mcpConnectors only if that input exists

Always notify users to create connectors and replace placeholder IDs before running the agent.

Phase 4: Present for Review

Present the complete agent configuration to the user:

• Agent metadata (name, description, uid)
• Full spec YAML
• Required connectors

Wait for explicit confirmation before creating/updating the agent.

Phase 5: Create or Update Agent

Only after confirmation, use harness_create to create a new agent or harness_update to update an existing one:

Creating a New Agent

Call MCP tool: harness_create
Parameters:
  resource_type: "agent"
  org_id: "<organization>"
  project_id: "<project>"
  body: {
    uid: "<generated_from_agent_name>",
    name: "<Agent Display Name>",
    description: "<Brief description of agent purpose>",
    spec: "<agent YAML spec as a string>",
    wiki: "<optional: markdown documentation>"
  }

Key fields for creation:

• uid (required): Unique identifier. Always generate from name as ca_<slug> and send explicitly (e.g. "Code Coverage Agent" → ca_code_coverage_agent). Do not omit it or rely on API-side auto-generation.
• name (required): Display name for the agent
• description (optional): Brief description
• spec (required): The full agent YAML specification as a string (includes version: 1, agent:, agent.step.group.steps, agent.inputs, and agent.layout)
• wiki (optional): Markdown documentation for the agent

Updating an Existing Agent

Call MCP tool: harness_update
Parameters:
  resource_type: "agent"
  resource_id: "<agent_identifier>"
  org_id: "<organization>"
  project_id: "<project>"
  body: {
    name: "<Updated Display Name>",           # optional
    description: "<Updated description>",     # optional
    spec: "<updated agent YAML spec>",        # optional
    wiki: "<updated markdown docs>"           # optional
  }

Key notes for updates:

• All fields in the body are optional — only provide fields you want to update
• Only custom agents (role='custom') can be updated; system agents cannot be modified
• The spec field replaces the entire agent specification when provided
• Use harness_get first to retrieve the current agent configuration before updating

Example: Code Review Agent

version: 1
agent:
  step:
    group:
      steps:
        - name: Agent
          if: <+Always>
          id: agent
          run:
            container:
              image: pkg.harness.io/vrvdt5ius7uwygso8s0bia/harness-agents/harness-ai-agent:latest
            env:
              PLUGIN_TASK: |
                Review the pull request for repository ${{inputs.repo_name}} on branch ${{inputs.branch}}.

                1. Analyze code changes for security vulnerabilities
                2. Check for code quality issues
                3. Verify test coverage
                4. Post review comments using GitHub MCP tools

                ## RULES
                - Focus on critical security issues first
                - Be constructive in feedback
                - Suggest specific code improvements
              PLUGIN_MAX_TURNS: 150
              PLUGIN_HARNESS_CONNECTOR: ${{inputs.llmConnector.id}}
              PLUGIN_ALLOWED_DOMAINS: ${{inputs.allowedDomains}}
              PLUGIN_MCP_FORMAT: harness
              PLUGIN_MCP_SERVERS: <+connectorInputs.resolveList(<+inputs.mcpConnectors>)>

  inputs:
    llmConnector:
      type: connector
      required: true
      default: your_llm_connector_id  # User must replace with actual connector ID
      ui:
        connectorCategories:
          - AI

    allowedDomains:
      type: string
      default: ""

    mcpConnectors:
      type: array
      default:
        - your_github_mcp_connector  # User must replace with actual connector ID
      ui:
        component: array
        input:
          inputType: connector
          inputConfig:
            connectorTypes:
              - Mcp

    repo_name:
      type: string
      default: my-org/my-repo

    branch:
      type: string
      default: main

  layout:
    - title: Agent Configuration
      items:
        - llmConnector
        - allowedDomains
        - mcpConnectors

Using Agents in Pipelines

Once an agent is created, it is published as a template and can be used in pipelines. The agent can be referenced in both v0 and v1 pipeline YAML formats.

v1 Pipeline YAML (Template-based)

In v1 pipelines, the agent template exposes its custom inputs (excluding llmConnector and mcpConnectors, which are handled internally).

Example: Using Code Review Agent in a v1 Pipeline

Assuming you created a "Code Review Agent" with custom inputs repo_name and branch:

pipeline:
  identifier: code_review_pipeline
  name: Code Review Pipeline
  inputs:
    repo_name:
      type: string
      default: my-org/my-repo
    branch:
      type: string
      default: feature/new-feature
  stages:
    - name: Code Review
      steps:
        - name: review_pr
          template:
            uses: ca_code_review_agent@1.0.0
            with:
              repo_name: <+inputs.repo_name>
              branch: <+inputs.branch>

Key points:

• Pipeline-level inputs: section defines runtime parameters
• uses: <agent_uid>@<version> references the published agent template
• with: block provides values for the agent's custom inputs using <+inputs.variableName> syntax
• llmConnector and mcpConnectors are configured at the agent level by default
• modelName is optional — only present in the agent if the user explicitly requested it
• Optionally, you can override llmConnector (and modelName if it exists) at the pipeline level if needed
• Only custom inputs (like repo_name, branch, thresholds, etc.) are passed in the pipeline

v0 Pipeline YAML (Agent Step Type)

In v0 pipelines, agents are referenced using the Agent step type, which internally references the v1 template. The step expands the v1 template and converts it to a v0 Run Step.

Example: Using Code Review Agent in a v0 Pipeline

pipeline:
  stages:
    - stage:
        type: Deployment
        spec:
          execution:
            steps:
              - stepGroup:
                  stepGroupInfra:
                    type: KubernetesDirect
                  steps:
                    - step:
                        type: Agent
                        name: ReviewPRAgent
                        identifier: ReviewPRAgent
                        spec:
                          agentName: ca_code_review_agent
                          agentSettings: |-
                            {
                              "repo_name": "my-org/my-repo",
                              "branch": "feature/new-feature",
                              "llmConnector": "your_llm_connector_id",
                              "modelName": "your_model_arn_or_id",
                              "mcpConnectors": ["your_github_mcp_connector", "your_slack_mcp_connector"]
                            }
                          llmConnector: your_llm_connector_id
                          mcpConnectors:
                            - your_github_mcp_connector

Key fields explained:

• type: Agent - Step type for v0 pipelines that references a v1 agent template
• name (required) - Display name for the step in the pipeline UI
• identifier (required) - Unique identifier for the step within the pipeline
• agentName (required) - The agent's UID (v1 template identifier) created via harness_create
• agentSettings (optional) - JSON string containing template inputs for the agent:
  • Custom inputs: Agent-specific fields like repo_name, branch, thresholds, etc.
  • llmConnector: LLM connector ID (overrides agent default and first-class field)
  • modelName: Model ARN or ID — only present if the agent was created with a modelName input
  • mcpConnectors: Array of MCP connector IDs (overrides agent default and first-class field)
• llmConnector (optional) - First-class field to specify LLM connector ID at pipeline level
• mcpConnectors (optional) - First-class array field to specify MCP connector IDs at pipeline level

Precedence rules:

1. Values in agentSettings JSON have highest precedence - they override both agent defaults and first-class fields
2. First-class fields (llmConnector, mcpConnectors) override agent defaults
3. If neither are provided, the agent's default configuration from the template is used

CRITICAL GUIDELINES

These are essential rules you MUST follow when creating/updating agents: