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Data Grounding & Multi-Agent Guide

High-stakes enterprise agents cannot rely on training data. They must be grounded in real-time business data and coordinate with specialized agents for complex tasks.

Two Pillars

Retriever Actions

Connecting Agents to Data Cloud

Retriever Actions connect your agent to Data Cloud Search Indexes, enabling context-aware knowledge retrieval.

Capabilities:

• ✅ Search Index wraps unstructured data (PDFs, docs, web pages)
• ✅ Chunking parses text, tables, and images into searchable segments
• ✅ Returns relevant chunks based on semantic similarity
• ✅ Integrated with Data Cloud for current data

Basic Retriever Action

actions:
  fetch_refund_policy:
    description: "Retrieve refund policy from knowledge base"
    target: "retriever://RefundSOP_Retriever"
    inputs:
      query: string
    outputs:
      chunks: list[object]
      relevance_scores: list[number]

Retrieval Pipeline

┌────────────┐    ┌────────────────┐    ┌─────────────────┐    ┌─────────────────┐    ┌──────────────┐
│ User Query │ → │ Dynamic Filter │ → │ Data Cloud Index│ → │ Relevant Chunks │ → │ LLM Response │
└────────────┘    └────────────────┘    └─────────────────┘    └─────────────────┘    └──────────────┘

Filtered Retrieval with Flows

Agent Script cannot filter retrieval results inline. When you need filtered knowledge retrieval, wrap the retriever in a Flow.

Problem: Direct retriever:// targets don't support inline filtering.

Solution: Use flow:// to wrap the retriever with filter logic.

# WRONG - No filtering capability
actions:
  fetch_policy:
    target: "retriever://RefundSOP_Index"  # Can't filter!

# CORRECT - Flow handles filtering
actions:
  fetch_regional_policy:
    description: "Fetch policy filtered by customer region"
    inputs:
      query: string
      customer_country: string
        description: "Country code for regional filtering"
    outputs:
      chunks: list[object]
      policy_region: string
    target: "flow://GetRegionalRefundPolicy"  # Flow filters internally

⚠️ Security Note: Filtering in a Flow prevents accidental cross-region data exposure. The Flow enforces the filter - the LLM cannot bypass it.

Safe Expressions

Agent Script uses a safe subset of Python for expressions:

Allowed Operations:

Security Constraints:

• ❌ No import statements
• ❌ No file access
• ❌ No arbitrary code execution

# Valid safe expressions:
@variables.risk_score > 80
@variables.country == "Germany"
@variables.is_vip and @variables.order_total > 100
not @variables.verified

# INVALID (security risk):
import os     # NOT ALLOWED
eval(...)     # NOT ALLOWED
open(...)     # NOT ALLOWED

The 6 Action Target Protocols

Every action uses a target: field to specify where to send the request.

Protocol 1: Salesforce Flow (flow://)

Use Case: Data operations, business logic, filtered retrieval

actions:
  get_order_status:
    description: "Retrieves order details by order number"
    inputs:
      order_number: string
        description: "The order ID to look up"
    outputs:
      status: string
      tracking_number: string
    target: "flow://GetOrderStatus"

Protocol 2: Apex Class (apex://)

Use Case: Custom calculations, complex validation

actions:
  calculate_refund:
    description: "Calculate refund amount based on policy rules"
    inputs:
      order_id: string
      refund_reason: string
    outputs:
      refund_amount: currency
      requires_approval: boolean
    target: "apex://RefundCalculatorService"

Protocol 3: Prompt Template (generatePromptResponse://)

Use Case: Grounded LLM responses, summarization

actions:
  summarize_case:
    description: "Generate a summary of the customer case"
    inputs:
      "Input:caseId": id
        description: "The Case record ID"
    outputs:
      promptResponse: string
        is_displayable: True
    target: "generatePromptResponse://Generate_Case_Summary"

⚠️ Note: Input keys use quoted strings with colon notation ("Input:caseId")

Protocol 4: Data Cloud Retriever (retriever://)

Use Case: Knowledge base search, FAQ lookup

actions:
  search_knowledge:
    description: "Search the knowledge base for relevant info"
    inputs:
      query: string
    outputs:
      chunks: list[object]
      relevance_scores: list[number]
    target: "retriever://RefundPolicy_Retriever"

Protocol 5: External Service (externalService://)

Use Case: Third-party APIs via Named Credentials

actions:
  verify_address:
    description: "Validate shipping address via external API"
    inputs:
      street: string
      city: string
      postal_code: string
    outputs:
      is_valid: boolean
      normalized_address: object
    target: "externalService://AddressValidation"

Protocol 6: Standard Invocable (standardInvocableAction://)

Use Case: Built-in Salesforce actions (email, tasks, Chatter)

actions:
  send_email:
    description: "Send confirmation email to customer"
    inputs:
      recipient_email: string
      template_id: id
    outputs:
      success: boolean
    target: "standardInvocableAction://emailSimple"

Protocol Selection Guide

Same Org Multi-Agent (SOMA)

When a primary agent encounters specialized needs, it can coordinate with expert agents.

Two Coordination Patterns

Pattern 1: Delegation

Flow:

┌─────────┐    DELEGATE    ┌────────────┐    RETURN    ┌─────────┐
│ Primary │ ─────────────▶ │ Specialist │ ───────────▶ │ Primary │
└─────────┘                └────────────┘              └─────────┘

Use Cases:

• Tax questions → Compliance Agent
• Technical issues → Support Specialist
• Order changes → Fulfillment Agent

Implementation:

# Delegation uses transition to a specialist topic
# Control returns after specialist completes
reasoning:
  actions:
    ask_compliance: @utils.transition to @topic.compliance
      description: "Delegate tax questions to specialist"
      # Returns here after specialist finishes

    ask_refund_help: @utils.transition to @topic.refund_specialist
      description: "Delegate refund calculations"
      # Control returns to continue conversation

Pattern 2: Handoff

Flow:

┌─────────┐    HANDOFF    ┌────────┐    NO RETURN
│ Primary │ ────────────▶ │ Target │ ────────────▶ ✗
└─────────┘               └────────┘

Use Cases:

• Escalation to human agent
• Domain boundary (Sales → Support)
• Fraud detection requiring specialized handling

Implementation:

# Permanent handoff - conversation leaves this agent
reasoning:
  actions:
    escalate_to_human: @utils.escalate
      description: "Transfer to live support"
      # Conversation ends for this agent

Delegation vs Handoff Decision

KEY INSIGHT: The difference is whether the original agent continues after the specialist finishes.

Variable Configuration for Grounding

Linked Variables for Session Context

variables:
  # WRONG - mutable allows modification
  CustomerCountry: mutable string = ""

  # CORRECT - linked preserves session data
  CustomerCountry: linked string
    source: @session.Country

Why This Matters:

• mutable string = "" starts empty - filter won't work
• linked string pulls from session - filter gets real value

Debug Exercise: Wrong Policy Applied

Symptom: German customer received US refund policy

Root Cause Trace:

Fix: Change mutable string = "" to linked string with source: @session.Country

Best Practices

1. Always Filter Regional Data

Never return unfiltered results for region-specific policies.

2. Use Flows for Complex Filtering

Agent Script can't filter inline - wrap retrievers in Flows.

3. Validate Session Variables

Ensure linked variables have sources - empty values cause wrong retrievals.

4. Choose Delegation vs Handoff Carefully

Delegation returns control; handoff is permanent.

5. Use available when for Sensitive Protocols

Guard external service calls with verification checks.

actions:
  call_payment_api: @actions.charge_card
    target: "externalService://PaymentGateway"
    available when @variables.customer_verified == True