Agentforce Development

Procedures for building Agentforce AI agents. Architecture, syntax reference, metadata types, instruction guidelines, and context engineering principles live in the reference file.

@../_reference/AGENTFORCE_PATTERNS.md

When to Use

• Building Agentforce AI agents with Agent Script (.agent files)
• Generating and publishing authoring bundles via CLI
• Configuring agent topics, actions, or conversation instructions
• Creating custom Apex actions or Flow actions for Agentforce
• Testing agents with YAML test specs, CLI, or Testing Center
• Deploying agent metadata (GenAi types) to orgs

Agent Types

Development Approaches

Default to Agent Script for all new development.

Agent Script Development

File Structure

force-app/main/default/aiAuthoringBundles/My_Agent/
  My_Agent.agent              # Agent Script file
  My_Agent.bundle-meta.xml    # Metadata XML

CLI Workflow

# Generate authoring bundle (from spec or --no-spec for boilerplate)
sf agent generate authoring-bundle --spec specs/agentSpec.yaml \
    --name "My Agent" --api-name My_Agent

# Edit .agent file in VS Code (syntax highlighting + validation)
# Validate → Publish → Preview → Activate
sf agent validate authoring-bundle --target-org MySandbox
sf agent publish authoring-bundle --target-org MySandbox
sf agent preview --target-org MySandbox
sf agent activate --api-name My_Agent --target-org MySandbox

What publish does: sf agent publish authoring-bundle validates the .agent file, auto-generates all downstream metadata (Bot, BotVersion, GenAiPlannerBundle, GenAiPlugin per topic), and wires action references. You do NOT need to manually create GenAiFunction metadata — the target: field resolves directly against the invocable action registry.

Complete Example

config:
   developer_name: "Service_Agent"
   agent_label: "Customer Service Agent"
   agent_type: "AgentforceServiceAgent"
   default_agent_user: "agentuser@example.com"
   description: "Service agent with custom Apex actions."

variables:
   customer_email: mutable string = ""
   is_verified: mutable boolean = False
   EndUserId: linked string
      source: @MessagingSession.MessagingEndUserId
      description: "MessagingEndUser Id"
   RoutableId: linked string
      source: @MessagingSession.Id
      description: "MessagingSession Id"
   ContactId: linked string
      source: @MessagingEndUser.ContactId
      description: "MessagingEndUser ContactId"
   EndUserLanguage: linked string
      source: @MessagingSession.EndUserLanguage
      description: "MessagingSession EndUserLanguage"

language:
   default_locale: "en_US"
   additional_locales: ""
   all_additional_locales: False

system:
   instructions: |
      You are a customer service agent. Be friendly and concise.
      Always verify identity before accessing account data.
   messages:
      welcome: "Hello! How can I help you today?"
      error: "I apologize. Let me transfer you to a team member."

start_agent topic_selector:
   label: "Topic Selector"
   description: "Routes messages to the appropriate topic"
   reasoning:
      instructions: ->
         | Analyze the message and determine the best topic.
      actions:
         verify: @utils.transition to @topic.identity
            description: "Verify customer identity"
         orders: @utils.transition to @topic.order_management
            description: "Help with order status or modifications"
         escalate: @utils.escalate
            description: "Transfer to human agent"

topic identity:
   label: "Identity Verification"
   description: "Verifies customer identity via email lookup"

   actions:
      lookup_customer:
         description: "Look up customer by email"
         inputs:
            email: string
               description: "Customer email address"
               is_required: True
         outputs:
            found: boolean
               description: "Whether a matching customer was found"
               is_used_by_planner: True
         target: "apex://LookupCustomerAction"

   reasoning:
      instructions: ->
         if @variables.is_verified == True:
            | Customer is already verified.
            transition to @topic.order_management
         | Ask for their email address to verify identity.
      actions:
         lookup: @actions.lookup_customer
            with email = ...
            set @variables.is_verified = @outputs.found

   after_reasoning: ->
      if @variables.is_verified == True:
         transition to @topic.order_management

topic order_management:
   label: "Order Management"
   description: "Helps customers check order status"

   actions:
      get_order_details:
         description: "Retrieve order details by order number"
         inputs:
            order_number: string
               description: "The order number to look up"
               is_required: True
               is_user_input: True
            customer_email: string
               description: "Customer email for verification"
               is_required: True
         outputs:
            status: string
               description: "Current order status"
               is_displayable: True
               is_used_by_planner: True
            tracking_number: string
               description: "Shipping tracking number"
               is_displayable: True
         target: "apex://GetOrderDetailsAction"

   reasoning:
      instructions: ->
         if @variables.is_verified == False:
            transition to @topic.identity
         | Help the customer with their order inquiry.
      actions:
         get_order: @actions.get_order_details
            with order_number = ...
            with customer_email = @variables.customer_email

Two-block pattern: Actions are declared in a top-level actions: block (with target, inputs, outputs) and referenced in reasoning.actions via @actions.<name> with with/set. The target: field resolves to the underlying implementation. In namespaced orgs, prefix: target: "apex://ns__ClassName" (check sfdx-project.json).

Key Patterns

Deterministic (->) — guaranteed execution:

-> if @variables.is_verified == False:
     transition to @topic.identity

LLM prompts (|) — accumulated and sent to model:

| Help the customer with their order.
| Be concise and provide the order number.

Deterministic action (run) — bypasses LLM:

-> run @actions.get_recent_orders
      with customer_id = @variables.customer_id
      set @variables.recent_orders = @outputs.orders

Action Types

Apex @InvocableMethod

Apex actions are built by sf-apex-agent using patterns from sf-apex-constraints. Key Agentforce-specific requirements:

• Labels/descriptions are critical — the LLM reads these to decide routing
• InvocableVariable descriptions must specify data type and format: "accountId — 18-digit Account record ID"
• Return result objects (not void) — agent needs structured confirmation
• Use Database class (partial success) not DML verbs (all-or-nothing)
• Varied verb names: "Locate", "Retrieve", "Calculate" — not "Get X", "Get Y"
• Decompose complex actions to avoid CPU timeout (10s sync limit)
• Long-running work: enqueue Queueable, return requestId

Other Action Types

MCP Server, Named Query, AuraEnabled, Apex Citations, Lightning Types, Adaptive Response Formats, Flow Actions, and Prompt Templates are documented in:

@_reference/ACTION_TYPES.md

For MCP Server actions specifically, see skill sf-agentforce-mcp-actions.

Testing

Apex unit tests for @InvocableMethod actions are handled by sf-apex-agent using sf-testing-constraints. This section covers agent-level testing — verifying topic routing, action execution, and response quality.

Agent Test Spec (YAML)

name: Service_Agent_Tests
description: End-to-end tests for Customer Service Agent
subjectType: AGENT
subjectName: Service_Agent
subjectVersion: v1
testCases:
  - utterance: "What's the status of order #12345?"
    expectedTopic: Order_Management
    expectedActions:
      - get_order_details
    expectedOutcome: "Agent provides order status details including shipping info"
    contextVariables:
      - name: EndUserLanguage
        value: en
    metrics:
      - topic_sequence_match
      - action_sequence_match
      - bot_response_rating
      - coherence
      - completeness
      - conciseness
      - latency

  - utterance: "I need to file a complaint about my delivery"
    expectedTopic: Case_Management
    expectedActions:
      - create_support_case
    expectedOutcome: "Agent creates a case and provides the case number"
    metrics:
      - topic_sequence_match
      - action_sequence_match
      - bot_response_rating
      - instructionAdherence

  - utterance: "Hola, necesito ayuda con mi pedido"
    expectedTopic: Order_Management
    contextVariables:
      - name: EndUserLanguage
        value: es
    metrics:
      - topic_sequence_match
      - coherence

Multi-Turn Conversation Testing

testCases:
  - utterance: "I want to return my order"
    expectedTopic: Order_Management
    conversationHistory:
      - role: user
        message: "Hi, I need help"
      - role: agent
        message: "Hello! How can I help you today?"
        topic: topic_selector
      - role: user
        message: "I have a problem with order #12345"
      - role: agent
        message: "I found order #12345. It was delivered on March 15."
        topic: Order_Management
    metrics:
      - topic_sequence_match
      - action_sequence_match

Custom Evaluations

testCases:
  - utterance: "What's the weather in SF?"
    customEvaluations:
      - label: Temperature Check
        jsonPathExpression: $.actions[0].result.temperature
        comparisonOperator: greaterThan
        expectedValue: "0"

CLI Test Workflow

# Generate test spec from agent definition
sf agent generate test-spec --output-file specs/testSpec.yaml

# Create test in org from YAML spec
sf agent test create --spec specs/testSpec.yaml --target-org MySandbox

# Run tests synchronously with JUnit output (CI-friendly)
sf agent test run --api-name Service_Agent_Tests --wait 10 \
    --result-format junit --output-dir ./test-results \
    --target-org MySandbox

# View results
sf agent test results --test-id <id> --target-org MySandbox

# List all agent tests
sf agent test list --target-org MySandbox

Testing Tools Summary

Metadata & Deployment

force-app/main/default/
  aiAuthoringBundles/My_Agent/   # .agent + .bundle-meta.xml
  bots/My_Agent/                 # .bot-meta.xml
  botVersions/My_Agent.v1/      # .botVersion-meta.xml
  genAiPlannerBundles/My_Agent/  # .genAiPlannerBundle-meta.xml (auto-generated by publish)
  genAiPlugins/Topic_Name/       # .genAiPlugin-meta.xml
  genAiFunctions/Action_Name/    # .genAiFunction-meta.xml (Classic UI only — Agent Script wires via target:)
  aiEvaluationDefinitions/       # .aiEvaluationDefinition-meta.xml

Deploy order (Classic UI): Bot/BotVersion → GenAiPromptTemplate → GenAiFunction → GenAiPlugin → GenAiPlannerBundle → AiAuthoringBundle → AiEvaluationDefinition → Activate

Deploy order (Agent Script): Deploy Apex @InvocableMethod classes → sf agent publish authoring-bundle (handles everything else)

Retrieve: sf project retrieve start --metadata "AiAuthoringBundle:My_Agent*"

Security

• Always use with sharing and AccessLevel.USER_MODE / WITH USER_MODE
• Ground Prompt Templates only with fields the user's profile can read
• Review agent conversations in Setup > Agent Conversations

Classic Topics (Pre-Agent Script)

For orgs on API < v65, configure topics in Agentforce Builder UI with classification description, WILL/WILL NOT scope, and numbered instructions (positive framing). All instruction guidelines and context engineering principles from the reference apply identically.

Related

• Agent: sf-agentforce-agent — for interactive guidance
• Constraints: sf-apex-constraints
• Reference: @../_reference/AGENTFORCE_PATTERNS.md