afv-library/skills/agentforce-grid/SKILL.md

name

description

agentforce-grid

Use for Agentforce Grid (AI Workbench) — the spreadsheet-like interface for AI operations in Salesforce. ALWAYS use this skill when the user wants to: test or evaluate an Agentforce agent with utterances; create workbooks, worksheets, or columns; enrich Salesforce records with AI-generated content; add AI columns (SINGLE_SELECT, PLAIN_TEXT), Formula columns, Object columns, Evaluation columns, or Reference columns; import CSV data; compare agent versions; debug column config errors; query SObjects or Data Cloud in a grid; run evaluations (coherence, topic assertion, response match); or use any mcp__grid-connect__ tools. Covers the Grid Connect API for all column CRUD, cell operations, agent testing pipelines, and batch processing. Skip for standalone Apex, Flows, dashboards, reports, validation rules, or Einstein Bots that don't involve Grid.

Agentforce Grid Skill

Agentforce Grid (AI Workbench) is a spreadsheet-like interface for AI operations in Salesforce. Worksheets contain typed columns that query data, run agents, generate AI content, and evaluate outputs.

Hierarchy: Workbook > Worksheet > Columns > Rows > Cells

Note: The MCP tool surface is being consolidated from ~65 tools to ~15. Tool names below reflect the current state. If a tool is not found, check get_supported_types or get_column_types for alternatives.

Before You Act (Mandatory Checklist)

Run this checklist before creating or suggesting any column:

1. Read current grid state -- call get_worksheet_data to see all existing columns, their IDs, and cell data
2. Check for duplicates -- does a column with the same name or purpose already exist? If so, suggest editing it (edit_ai_prompt, edit_column) instead of creating a new one
3. Verify all referenced column IDs exist -- every columnId in referenceAttributes, inputColumnReference, or referenceColumnReference MUST come from the current grid state. NEVER hallucinate column IDs or reference columns that do not exist yet
4. Confirm column type matches the task -- use the decision table below
5. Confirm response format matches the use case -- SINGLE_SELECT for filtering/sorting, PLAIN_TEXT for free-form content
6. Check dependency order -- source columns must exist before processing columns that reference them
7. Check available resources -- call get_prompt_templates or get_invocable_actions before creating an AI column that could be handled by existing platform capabilities

Column Types

Type	type value	Use Case
AI	"AI"	LLM text generation with custom prompts
Agent	"Agent"	Run agent conversations
AgentTest	"AgentTest"	Batch-test agent with utterances
Evaluation	"Evaluation"	Evaluate agent/prompt outputs (13 types)
Formula	"Formula"	Deterministic computed values
Object	"Object"	Query Salesforce SObjects
DataModelObject	"DataModelObject"	Query Data Cloud DMOs
PromptTemplate	"PromptTemplate"	Execute GenAI prompt templates
InvocableAction	"InvocableAction"	Execute Flows or Apex
Action	"Action"	Execute platform actions
Reference	"Reference"	Extract fields via JSON path
Text	"Text"	Static/editable text or CSV import

Casing: Server is case-insensitive. PascalCase and UPPER_CASE both work. API returns vary by context.

For complete JSON configs: Column Configs Reference

Choosing the Right Column Type

Need	Type	Format	Why
Categorize/filter/sort	AI	SINGLE_SELECT	Limited options enable filtering and scanning
Generate free-text (email, summary)	AI	PLAIN_TEXT	Open-ended content needs free-form output
Deterministic computation	Formula	N/A	No LLM needed, exact and reproducible
Extract field from JSON/Object	Reference	N/A	Zero LLM cost, exact extraction
Score/rate on a scale	AI	SINGLE_SELECT	e.g., High/Medium/Low for scannable output
Task already handled by a prompt template	PromptTemplate	N/A	Pre-built, tested, maintained by the org
Task already handled by a Flow	InvocableAction	N/A	Deterministic logic, no LLM cost

Key principle: If output is used for filtering, sorting, or downstream comparison, use SINGLE_SELECT. Free-text defeats scanability. If a platform capability (prompt template, Flow, list view) already does the job, prefer it over AI.

Role-Aware Suggestions

Before suggesting columns, understand the user's role and intent. Ask if unclear.

Role	Common Needs	Pipeline Pattern
Sales Rep	Deal risk, competitive intel, account priority	Object(Opps) -> AI(risk, SINGLE_SELECT)
CSM	Customer health, check-in emails	Object(Accounts/Cases) -> AI(analysis) -> AI(email, PLAIN_TEXT)
RevOps	Pipeline quality, data hygiene flags	Object(Opps) -> AI(quality flag, SINGLE_SELECT: Clean/Minor Issues/Needs Fix)
Dev/QA	Agent testing, flow testing	Text(utterances) -> AgentTest -> Evaluation
Admin	Data enrichment, bulk updates	Object/ListView -> AI -> Action(RecordUpdate)

A RevOps user needs data quality flags (SINGLE_SELECT), not outreach emails. A CSM needs customer-facing outputs, not pipeline metrics.

Evaluation Types

Type	Needs Reference Column	Supported Inputs
COHERENCE	No	Agent, AgentTest, PromptTemplate
CONCISENESS	No	Agent, AgentTest, PromptTemplate
FACTUALITY	No	Agent, AgentTest, PromptTemplate
INSTRUCTION_FOLLOWING	No	Agent, AgentTest, PromptTemplate
COMPLETENESS	No	Agent, AgentTest, PromptTemplate
RESPONSE_MATCH	Yes	Agent, AgentTest
TOPIC_ASSERTION	Yes	Agent, AgentTest
ACTION_ASSERTION	Yes	Agent, AgentTest
LATENCY_ASSERTION	No	Agent, AgentTest
BOT_RESPONSE_RATING	Yes	Agent, AgentTest
EXPRESSION_EVAL	No	Agent, AgentTest
CUSTOM_LLM_EVALUATION	Yes	Agent, AgentTest
TASK_RESOLUTION	No	Agent, AgentTest (conversation-level)

For complete evaluation guidance: Evaluation Types Reference

Dependency Rules

Column creation must follow the dependency DAG. A column CANNOT reference a column that does not yet exist or that depends on it.

1. Source data (Text, Object, DataModelObject) -- no dependencies
2. Processing (AI, Agent, AgentTest, PromptTemplate, InvocableAction) -- depend on source
3. Extraction (Reference) -- depends on processing columns
4. Assessment (Evaluation) -- depends on Agent/AgentTest/PromptTemplate
5. Formula -- any level, but must only reference existing columns

Always create columns sequentially. Each add_column returns the new column ID needed by subsequent columns. Parallel creation leads to missing IDs and unpredictable ordering.

Leverage Existing Resources

Before creating an AI column, check if platform capabilities handle the task:

1. get_prompt_templates -- use PromptTemplate column for pre-built prompts
2. get_invocable_actions -- use InvocableAction column for deterministic logic
3. get_list_views -- use list view SOQL in Object column's advancedMode
4. Existing columns -- use Reference column to extract data already present

AI columns should be reserved for tasks requiring LLM reasoning.

Critical Config Rules

Nested config structure (REQUIRED for ALL column types):

{
  "name": "Column Name",
  "type": "AI",
  "config": {
    "type": "AI",
    "queryResponseFormat": {"type": "EACH_ROW"},
    "autoUpdate": true,
    "config": {
      "autoUpdate": true
    }
  }
}

Even Text columns need type inside config. An empty config: {} will fail.

queryResponseFormat: Use EACH_ROW when adding a column to a worksheet that already has data. Use WHOLE_COLUMN with splitByType: "OBJECT_PER_ROW" only when importing new records (Object/DataModelObject).

modelConfig: Use the model name for both modelId and modelName. Call get_llm_models to list available models. Default: sfdc_ai__DefaultGPT4Omni.

AI columns: Require mode: "llm", responseFormat with options array (empty [] for PLAIN_TEXT).

Evaluation columns: Types requiring reference columns (RESPONSE_MATCH, TOPIC_ASSERTION, ACTION_ASSERTION, BOT_RESPONSE_RATING, CUSTOM_LLM_EVALUATION) must include referenceColumnReference.

For complete JSON configs for all 12 types: Column Configs Reference

Common Patterns

Agent Testing Pipeline

Text(utterances) -> Text(expected) -> AgentTest -> Evaluation(RESPONSE_MATCH) -> Evaluation(COHERENCE)

Quick path: Use setup_agent_test for one-call creation of the full pipeline.

Data Enrichment

Object(Accounts) -> AI(summary, PLAIN_TEXT) -> AI(sentiment, SINGLE_SELECT)

Flow/Apex Testing

Text(inputs) -> InvocableAction(Flow) -> Reference(extract output field)

For complete step-by-step MCP tool call examples: Use Case Patterns Reference

SF CLI Setup & Authentication

Before using the Grid API, authenticate to a Salesforce org using the SF CLI.

sf --version                    # Check if installed
sf org login web --alias my-org # Authenticate (add --instance-url for specific org)
sf org display --target-org my-org --json  # Get access token + instance URL

Instance URL formats: https://sdbX.testX.pc-rnd.pc-aws.salesforce.com/ (internal), https://mycompany.my.salesforce.com (production), https://mycompany--sandbox.sandbox.my.salesforce.com (sandbox).

SF CLI does NOT accept lightning domains. If a user provides one (e.g., orgfarm-xxx.test1.lightning.pc-rnd.force.com), ask for the instance URL instead.

For full auth instructions: sf org login web --help

MCP Tool Reference

All tools use prefix mcp__grid-connect__. Grouped by operation type.

Orchestration (Start Here)

Tool	What It Does
apply_grid	Create/update a full grid from YAML DSL -- most powerful orchestration tool
setup_agent_test	One-call agent test pipeline: workbook + worksheet + columns + evaluations
create_workbook_with_worksheet	Create workbook + worksheet in one call
poll_worksheet_status	Poll until processing completes
get_worksheet_summary	Compact status overview
run_worksheet	Execute worksheet with optional runStrategy: "ColumnByColumn" for sequential

Read State

Tool	What It Does
get_workbooks	List all workbooks
get_workbook	Get workbook details
get_worksheet	Get worksheet metadata
get_worksheet_data	Primary read tool -- full data with all columns, rows, cells
get_worksheet_data_generic	Generic format variant
get_column_data	Get cell data for one column

Create & Modify

Tool	What It Does
create_workbook / create_worksheet	Create resources
add_column	Add column (returns new column ID)
edit_column	Update config AND reprocess all cells
save_column	Update config WITHOUT reprocessing
reprocess_column / reprocess	Reprocess cells (column or worksheet)
delete_column / delete_workbook / delete_worksheet	Delete resources
add_rows / delete_rows	Manage rows
update_cells	Update specific cells (use fullContent, not displayContent)
paste_data	Paste data matrix into grid
trigger_row_execution	Run processing for specific rows or cells
import_csv	Import CSV to worksheet

Typed Mutations (Prefer Over Raw edit_column)

These auto-fetch current config, merge changes, and handle references correctly.

Tool	What It Does
edit_ai_prompt	Edit AI column prompt, model, or response format
edit_agent_config	Edit Agent/AgentTest config
edit_prompt_template	Edit PromptTemplate column
change_model	Switch LLM model on AI or PromptTemplate column
add_evaluation	Add evaluation with auto-wired references
update_filters	Update Object/DMO query filters

Discovery

Tool	What It Does
get_agents	List agents (use includeDrafts for unpublished)
get_agent_variables	Get agent context variables
get_sobjects / get_sobject_fields_display / get_sobject_fields_filter	SObject discovery
get_dataspaces / get_data_model_objects / get_data_model_object_fields	Data Cloud discovery
get_llm_models	List available LLM models
get_evaluation_types	List evaluation types available in the org
get_column_types / get_supported_types	List column types
get_formula_functions / get_formula_operators	Formula building helpers
get_invocable_actions / describe_invocable_action	Discover Flows/Apex
get_prompt_templates / get_prompt_template	Discover prompt templates
get_list_views / get_list_view_soql	List view SOQL

AI Generation

Tool	What It Does
create_column_from_utterance	Create column from natural language description
generate_soql	Natural language to SOQL
generate_json_path	AI-assisted JSON path generation
generate_test_columns	Generate test column configs
generate_ia_input	Generate invocable action input payload
get_url	Generate Lightning Experience URLs

Tool Operation Distinctions

Tool	Behavior	When to Use
edit_column	Updates config AND reprocesses	Changing prompt, model, references
save_column	Updates config, NO reprocess	Renaming, display-only changes
reprocess_column	Reprocesses with current config	Source data changed, retrying failures

State Refresh

Call get_worksheet_data after any mutation (add_column, paste_data, trigger_row_execution) to get updated IDs and statuses. Use poll_worksheet_status for long-running operations.

Error Patterns

Situation	What to Do
Column creation returns error but column exists	Verify with get_worksheet_data -- column may have been created despite the error
Cell processing failures	Use reprocess_column or trigger_row_execution with failed row IDs
Duplicate column name	API rejects with DuplicateColumnName -- check existing columns first
Config validation error	Fix config per error message and retry

Known Limits

• 100 test suites per org, 20 runs per suite, 10 concurrent runs per org
• AI column batches: 25 rows/batch, 4 parallel threads for evaluations

Reference Documentation

• Column Configs -- Complete JSON for all 12 column types
• Evaluation Types -- All 13 evaluation types with guidance
• API Endpoints -- Complete endpoint docs with examples
• Use Case Patterns -- Common workflows with MCP tool call examples
• Workflow Patterns -- User interaction models, conversation examples, CI/CD patterns