developing-agentforce

name	developing-agentforce
description	Build, modify, debug, and deploy agents with Agentforce Agent Script. TRIGGER when: user creates, modifies, or asks about .agent files or aiAuthoringBundle metadata; changes agent behavior, responses, or conversation logic; designs agent topics, actions, tools, sub-agents, or flow control; writes or reviews an Agent Spec; previews, debugs, deploys, publishes, or tests agents; uses Agent Script CLI commands (sf agent generate/preview/publish/test). DO NOT TRIGGER when: Apex development, Flow building, Prompt Template authoring, Experience Cloud configuration, or general Salesforce CLI tasks unrelated to Agent Script.
license	Apache-2.0
compatibility	Requires Agentforce license, API v66.0+, Einstein Agent User
metadata	{"version":"0.4.10","last_updated":"2026-03-25"}

Agent Script Skill

What This Skill Is For

Agent Script is Salesforce's scripting language for authoring next-generation AI agents on the Atlas Reasoning Engine. Introduced in 2025 with zero training data in any AI model. Everything needed to write, modify, diagnose, or deploy Agent Script agents is in this skill's reference files.

⚠️CRITICAL: Agent Script is NOT AppleScript, JavaScript, Python, or any other language. Do NOT confuse Agent Script syntax or semantics with any other language you have been trained on.

Agent Script agents are defined by AiAuthoringBundle metadata — a directory with a .agent file containing Agent Script source that describes topics, actions, instructions, flow control, and configuration; and a bundle-meta.xml file containing bundle metadata. Agents process utterances by routing through topics, each with instructions and actions backed by Apex, Flows, Prompt Templates, and other types of backing logic.

This skill covers the full Agent Script lifecycle: designing agents, writing Agent Script code, validating and debugging, deploying and publishing, and testing.

How to Use This Skill

This file maps user intent to task domains and relevant reference files in references/. Detailed knowledge includes syntax rules, design patterns, CLI commands, debugging workflows, and more.

Identify user intent from task descriptions. ALWAYS read indicated reference files BEFORE starting work.

Rules That Always Apply

1. Always --json. ALWAYS include --json on EVERY sf CLI command. Do NOT pipe CLI output through jq or 2>/dev/null. Read the full JSON response directly — LLMs parse JSON natively.

2. Verify target org. Before any org interaction, run sf config get target-org --json to confirm a target org is set. If none configured, ask the user to set one with sf config set target-org <alias>.

3. Diagnose before you fix. When validating/debugging agent behavior, ALWAYS --use-live-actions to preview authoring bundles. Send utterances then read resulting session traces to ground your understanding of the agent's behavior. Trace files reveal topic selection, action I/O, and LLM reasoning. DO NOT modify .agent files or backing logic without this grounding. See Validation & Debugging for trace file locations and diagnostic patterns.

4. Spec approval is a hard gate. Never proceed past Agent Spec creation without explicit user approval.

Task Domains

Every task domain below has Required Steps. Follow verbatim, in order. Do not substitute your own plan or skip steps.

Create an Agent

User wants to build new agent from scratch. ALWAYS use Agent Script. Work with User to understand the agent's purpose, topics, and actions using plain language without Salesforce-specific terminology.

Required Steps

Read CLI for Agents for exact command syntax.

1. Design — Read Design & Agent Spec to draft an Agent Spec. Always ask if you should scan for existing backing logic. Unless instructed otherwise, scan by reading sfdx-project.json to identify package directories, then search each for @InvocableMethod in classes/, AutoLaunchedFlow in flows/, and template metadata in promptTemplates/. Mark matches EXISTS; unmatched actions NEEDS STUB. Also scan objects/ for .object-meta.xml to discover custom objects — related objects often contain data the agent should expose even when not mentioned in the prompt. Always save Agent Spec as file.
2. STOP for user approval of Agent Spec. Present to user. Ask for approval or feedback. Do not proceed without approval. Once approved, proceed without stopping unless a step fails.
3. Validate environment prerequisites — Read Design & Agent Spec, Section 3 (Environment Prerequisites). Based on agent type from design, validate org environment:
   • Employee agent: Confirm config block does NOT include default_agent_user, connection messaging:, or MessagingSession linked variables. Remove if present. See Examples for a complete employee agent example.
   • Service agent: Query org for Einstein Agent User. If one exists, confirm username with user. If none, guide user through creation. See CLI for Agents, Section 12 for creation steps and Agent User Setup for required permissions. Do not proceed to code generation until environment is validated.
4. Generate authoring bundle — sf agent generate authoring-bundle --json --no-spec --name "<Label>" --api-name <Developer_Name>
5. Write code — Read Core Language for syntax, block structure, and anti-patterns. Edit generated .agent file using reference files and templates. Do not create .agent or bundle-meta.xml files manually.
6. Validate compilation — sf agent validate authoring-bundle --json --api-name <Developer_Name> If validation fails, read Validation & Debugging to diagnose and fix, then re-validate. ALWAYS fix syntax and structural errors before generating backing logic.
7. Generate backing logic — For each action marked NEEDS STUB: sf template generate apex class --name <ClassName> --output-dir <PACKAGE_DIR>/main/default/classes Replace class body with invocable pattern from Design & Agent Spec. ALWAYS deploy: sf project deploy start --json --metadata ApexClass:<ClassName> ALWAYS fix deploy errors BEFORE generating and deploying next stub.
8. Validate behavior — Read Validation & Debugging for preview workflow and session trace analysis. sf agent preview start --json --use-live-actions --authoring-bundle <Developer_Name> If actions query data, ground test utterances with: sf data query --json -q "SELECT <Relevant_Fields> FROM <SObject> LIMIT 100" Send test utterances with: sf agent preview send --json --authoring-bundle <Developer_Name> --session-id <ID> -u "<message>" Confirm topic routing, gating, and action invocations match Agent Spec. If behavior diverges, switch to Diagnose Behavioral Issues workflow. Return AFTER correcting issues. CHECKPOINT — Do NOT proceed to Publish unless ALL are true:
   • validate authoring-bundle passes with zero errors
   • Live preview (--use-live-actions) tested with representative utterances per topic
   • Traces confirm correct topic routing and action invocation
   • User explicitly approves deployment
9. Publish — Publish validates metadata structure, not agent behavior. Every publish creates permanent version number. sf agent publish authoring-bundle --json --api-name <Developer_Name> If publish fails, follow troubleshooting checklist in Metadata & Lifecycle, Section 5 before retrying.
10. Activate — Makes new version available to users. sf agent activate --json --api-name <Developer_Name>
11. Verify published agent — Preview user-facing behavior AFTER activation with sf agent preview start --json --api-name <Developer_Name> Use --api-name, not --authoring-bundle.
12. Configure end-user access — ONLY for employee agents. Read Agent Access Guide to configure perms and assign access.

Reference Files

1. CLI for Agents — exact command syntax for generate, validate, deploy, publish, activate; Section 12 for Einstein Agent User creation
2. Core Language — execution model, syntax, block structure, anti-patterns
3. Design & Agent Spec — topic graph design, flow control patterns, Agent Spec production, backing logic analysis; Section 3 for environment prerequisites
4. Topic Map Diagrams — Mermaid diagram conventions for visualizing the agent's topic graph
5. Agent User Setup & Permissions — permission set assignment, object permissions, cross-topic validation
6. Metadata & Lifecycle — directory structure, bundle metadata; publish troubleshooting
7. Validation & Debugging — validate the agent compiles, preview to confirm behavior
8. Agent Access Guide — end-user access permissions, visibility troubleshooting
9. Known Issues — only load when errors persist after code fixes
10. Architecture Patterns — hub-and-spoke, verification gate, post-action loop
11. Complex Data Types — type mapping decision tree
12. Safety Review — 7-category safety review
13. Discover Reference — target discovery CLI
14. Scaffold Reference — stub generation CLI
15. Deploy Reference — deployment lifecycle, error recovery

Comprehend an Existing Agent

User wants to understand Agent Script agent they didn't write or need to revisit. May point to AiAuthoringBundle directory or ask "what does this agent do?" or "I need to fix this agent but I don't understand how it works.".

Required Steps

1. Locate agent — Read sfdx-project.json to identify package directories. Find AiAuthoringBundle directory within them. Read .agent file and bundle-meta.xml.
2. Read code — Read Core Language for syntax and execution model BEFORE parsing .agent file.
3. Map backing logic — For each action with target, locate backing implementation (Apex class, Flow, Prompt Template) in project. Note input/output contracts.
4. Reverse-engineer Agent Spec — Read Design & Agent Spec for Agent Spec structure. Produce Agent Spec from code and save as file.
5. Produce Topic Map diagram — Read Topic Map Diagrams for Mermaid conventions. Generate flowchart of topic graph showing transitions, gates, and action associations.
6. Annotate source — Ask if user wants Agent Script source annotated with explanations. If requested, add inline comments to .agent file explaining flow control decisions, gating rationale, and topic relationships.
7. Present to user — Share Agent Spec, Topic Map, and annotated source if produced. Check Anti-Patterns section in Core Language reference and flag any matches found in code.

Reference Files

1. Core Language — syntax, execution model, anti-patterns
2. Design & Agent Spec — Agent Spec structure, flow control pattern recognition
3. Topic Map Diagrams — Mermaid conventions for topic graph visualization
4. Metadata & Lifecycle — directory conventions, bundle metadata
5. Known Issues — only load when code contains unexplained workaround patterns

Modify an Existing Agent

User wants to add, remove, or change topics, actions, instructions, or flow control on existing agent. May describe change in plain language ("add a billing topic") or reference specific Agent Script constructs.

Required Steps

Read CLI for Agents for exact command syntax.

1. Comprehend — If no Agent Spec exists, reverse-engineer first by following "Comprehend an Existing Agent" workflow above.
2. Update Agent Spec — Read Design & Agent Spec for flow control patterns and backing logic analysis. Modify Agent Spec to reflect intended changes. For new actions, always ask if you should scan for existing backing logic. Unless instructed otherwise, scan by reading sfdx-project.json to identify package directories, then search each for @InvocableMethod in classes/, AutoLaunchedFlow in flows/, and template metadata in promptTemplates/. Mark matches EXISTS; unmatched actions NEEDS STUB. Always save updated Agent Spec as file.
3. STOP for user approval of updated Agent Spec. Present to user. Ask for approval or feedback. Do not proceed without approval. Once approved, proceed without stopping unless a step fails.
4. Edit code — Read Core Language for syntax and anti-patterns. Edit .agent file to implement approved changes.
5. Validate compilation — sf agent validate authoring-bundle --json --api-name <Developer_Name> If validation fails, read Validation & Debugging to diagnose and fix, then re-validate.
6. Generate new backing logic — For each new action marked NEEDS STUB: sf template generate apex class --name <ClassName> --output-dir <PACKAGE_DIR>/main/default/classes Replace class body with invocable pattern from Design & Agent Spec. ALWAYS deploy: sf project deploy start --json --metadata ApexClass:<ClassName> ALWAYS fix deploy errors BEFORE generating and deploying next stub. Skip if no new actions added.
7. Validate behavior — Read Validation & Debugging for preview workflow and session trace analysis. sf agent preview start --json --use-live-actions --authoring-bundle <Developer_Name> If actions query data, ground test utterances with: sf data query --json -q "SELECT <Relevant_Fields> FROM <SObject> LIMIT 100" Send test utterances with: sf agent preview send --json --authoring-bundle <Developer_Name> --session-id <ID> -u "<message>" Test changed paths first, then adjacent paths to catch regressions in existing behavior. CHECKPOINT — Do NOT proceed to Publish unless ALL are true:
   • validate authoring-bundle passes with zero errors
   • Live preview (--use-live-actions) tested with representative utterances per topic
   • Traces confirm correct topic routing and action invocation
   • User explicitly approves deployment
8. Publish — Publish validates metadata structure, not agent behavior. Every publish creates permanent version number. sf agent publish authoring-bundle --json --api-name <Developer_Name> If publish fails, follow troubleshooting checklist in Metadata & Lifecycle, Section 5 before retrying.
9. Activate — Makes new version available to users. sf agent activate --json --api-name <Developer_Name>
10. Verify published agent — Preview user-facing behavior AFTER activation with sf agent preview start --json --api-name <Developer_Name> Use --api-name, not --authoring-bundle.

Reference Files

1. CLI for Agents — exact command syntax for validate, deploy, preview, publish, activate
2. Core Language — syntax, anti-patterns
3. Design & Agent Spec — Agent Spec updates, backing logic analysis
4. Validation & Debugging — compilation diagnosis, preview workflow, session trace analysis
5. Known Issues — only load when errors persist after code fixes

Diagnose Compilation Errors

User has Agent Script that won't compile. Errors surface from sf agent validate or sf agent preview start, or User describes symptoms like "I'm getting a validation error."

Required Steps

Read CLI for Agents for exact command syntax.

1. Reproduce error — Run sf agent validate authoring-bundle --json --api-name <Developer_Name> to capture basic compile errors. If no errors, run sf agent preview start --json --use-live-actions --authoring-bundle <Developer_Name> to capture complex compile errors. If user provides specific error output, ALWAYS reproduce to confirm.
2. Classify error — Read Validation & Debugging for error taxonomy. Map each error message to root cause category.
3. Locate fault — Read Core Language to understand correct syntax. Find specific line(s) in .agent file that cause each error.
4. Fix code — Apply targeted fixes. Check Anti-Patterns section in Core Language reference to ensure you're not introducing known bad pattern.
5. Re-validate — Run sf agent validate authoring-bundle --json --api-name <Developer_Name> then run sf agent preview start --json --use-live-actions --authoring-bundle <Developer_Name> Repeat steps 2–5 if errors persist.
6. Explain fix — Tell user what was wrong and what you changed. Explain root cause in terms of Core Language agent execution model.

Reference Files

1. Core Language — syntax, block structure, anti-patterns
2. Validation & Debugging — error taxonomy, error-to-root-cause mapping
3. Known Issues — only load when error doesn't match user code; may be a platform bug
4. Production Gotchas — only load when error involves reserved keywords or lifecycle hook syntax

Diagnose Behavioral Issues

Agent compiles, preview can start and --use-live-actions, but agent does not behave as expected. User describes symptoms like "the agent keeps going to the wrong topic" or "the action isn't being called." Fundamentally different from validate or preview start errors — code is valid but behavior is wrong.

Required Steps

Read CLI for Agents for exact command syntax.

1. Establish baseline — Read Agent Spec. If no Agent Spec exists, follow Comprehend an Existing Agent workflow to reverse-engineer one, then continue.
2. Form hypotheses — Read Core Language for execution model. Based on user's description, list candidate root causes. Think through: topic routing, gating conditions, action availability, instruction clarity, variable state, and transition timing.
3. Reproduce in preview — Read Validation & Debugging for preview workflow and session trace analysis. Start preview session: sf agent preview start --json --use-live-actions --authoring-bundle <Developer_Name> then send test messages covering EACH topic with sf agent preview send. One message is not enough — confirm behavior per topic before proceeding.
4. Analyze session traces — Examine trace output to confirm topic selection, action availability/execution, LLM reasoning, and where behavior diverges from Agent Spec. Do NOT skip this step — preview output alone is insufficient for diagnosis.
5. Identify root cause — Match trace evidence to hypotheses. Consult Core Language reference and Gating Patterns in Design & Agent Spec reference to confirm absence of anti-patterns.
6. Fix code — Apply targeted fix. If fix involves flow control changes, update Agent Spec to match.
7. Re-validate and re-preview — Repeat steps 3–6 until behavior matches Agent Spec or you confirm a platform limitation. Run validate authoring-bundle, then preview start --use-live-actions to verify fix using same utterances. Then test adjacent paths that might be affected by your changes.
8. Explain fix — Tell user what was wrong and what you changed. Explain root cause in terms of Core Language agent execution model.

Reference Files

1. Core Language — execution model, anti-patterns
2. Design & Agent Spec — Agent Spec as behavioral baseline, gating patterns
3. Validation & Debugging — preview workflow, session trace analysis
4. Known Issues — only load when behavior is wrong but code logic is correct

Deploy, Publish, and Activate

User wants to take working agent from local development to running state in Salesforce org. Involves deploying AiAuthoringBundle and its dependencies, publishing to commit version, then activating to make it live.

Required Steps

Read CLI for Agents for exact command syntax.

1. Validate compilation — sf agent validate authoring-bundle --json --api-name <Developer_Name> Do not proceed if validation fails.
2. Deploy bundle and dependencies — Read Metadata & Lifecycle for dependency management and deploy commands. Deploy AiAuthoringBundle and all backing logic (Apex classes, Flows, Prompt Templates) and dependencies to org.
3. Live preview — Read Validation & Debugging for preview workflow and session trace analysis. sf agent preview start --json --use-live-actions --authoring-bundle <Developer_Name> then send test utterances with: sf agent preview send --json --authoring-bundle <Developer_Name> --session-id <ID> -u "<message>" Test key conversation paths to validate agent behavior when backed by live actions. CHECKPOINT — Do NOT proceed to Publish unless ALL are true:
   • validate authoring-bundle passes with zero errors
   • Live preview (--use-live-actions) tested with representative utterances per topic
   • Traces confirm correct topic routing and action invocation
   • User explicitly approves deployment
4. Publish — Publish validates metadata structure, not agent behavior. DO NOT publish as part of a dev/test inner loop. ONLY publish as the FINAL step prior to activating the agent and surfacing it to end users. sf agent publish authoring-bundle --json --api-name <Developer_Name> If publish fails, follow Troubleshooting Publish Failures in Metadata & Lifecycle before retrying.
5. Activate — Makes new version available to users. sf agent activate --json --api-name <Developer_Name>
6. Verify published agent — Preview user-facing behavior AFTER activation with sf agent preview start --json --api-name <Developer_Name> Use --api-name, not --authoring-bundle.
7. Configure end-user access — ONLY for employee agents. Read Agent Access Guide to configure perms and assign access.

Reference Files

1. CLI for Agents — exact command syntax for deploy, publish, activate, deactivate
2. Validation & Debugging — compilation validation, preview workflow
3. Metadata & Lifecycle — dependency management, deploy commands; publish troubleshooting
4. Agent Access Guide — end-user access permissions, visibility troubleshooting
5. Known Issues — only load when deploy hangs, publish fails, or activate fails unexpectedly

Diagnose Production Issues

User's agent is published and active but experiencing issues not caught during preview. Includes credit overconsumption, token or size limit failures, loop guardrail interruptions, reserved keyword runtime errors, VS Code sync failures, or unexpected behavioral differences between preview and production.

Required Steps

Read CLI for Agents for exact command syntax.

1. Classify issue — Determine whether this is billing/cost concern, runtime limit, naming conflict, tooling issue, or behavioral difference between preview and production.
2. Check known production gotchas — Read Production Gotchas for credit consumption, token limits, loop guardrails, reserved keywords, lifecycle hooks, and VS Code workarounds.
3. Compare preview vs production behavior — If issue is behavioral, preview published agent with sf agent preview start --json --api-name <Developer_Name> (not --authoring-bundle). Compare against live-actions authoring bundle preview --authoring-bundle <Developer_Name> --use-live-actions to isolate preview-vs-production differences.
4. Check known issues — Read Known Issues for platform bugs that may explain production-only failures.
5. Fix and republish — Apply fixes, validate, re-preview, publish, activate, verify. Follow Deploy, Publish, and Activate steps.
6. Explain diagnosis — Tell user what was happening and what you changed. Explain root cause.

Reference Files

1. Production Gotchas — credit consumption, token limits, loop guardrails, reserved keywords, lifecycle hooks, VS Code workarounds
2. CLI for Agents — command syntax for preview, publish, activate
3. Validation & Debugging — preview workflow, session trace analysis
4. Known Issues — only load when issue may be a platform bug

Delete or Rename an Agent

User wants to remove agent or change its name. Maintenance tasks complicated by AiAuthoringBundle versioning and published version dependencies.

Required Steps

Read CLI for Agents for exact command syntax.

1. Understand current state — Read Metadata & Lifecycle for versioning, delete mechanics, and rename mechanics. Identify whether agent has been published, how many versions exist, and whether it's currently active.
2. Deactivate if active — sf agent deactivate --json --api-name <Developer_Name> Active agent cannot be deleted or renamed.
3. Execute operation — For delete: follow delete mechanics in Metadata & Lifecycle reference. For rename: follow rename mechanics in same reference.
4. Clean up orphans — Check for and remove orphaned metadata: Bot, BotVersion, GenAiPlannerBundle, GenAiPlugin, GenAiFunction. Metadata & Lifecycle reference details what to look for.
5. Validate — Confirm operation completed cleanly. For rename, validate new bundle compiles and preview to confirm behavior.

Reference Files

1. CLI for Agents — exact command syntax for delete, deactivate, retrieve
2. Validation & Debugging — compilation validation, preview workflow
3. Metadata & Lifecycle — delete mechanics, rename mechanics, orphan cleanup

Test an Agent

User wants to create automated tests for Agent Script agent. Involves writing AiEvaluationDefinition test specs in YAML format that define test scenarios, expected behaviors, and quality metrics.

Required Steps

Read CLI for Agents for exact command syntax.

1. Establish coverage baseline — Read Agent Spec. If no Agent Spec exists, reverse-engineer first by following Comprehend steps. Map every topic, action, and flow control path to identify what needs test coverage.
2. Design test scenarios — For test design methodology, expectations, metrics, test spec YAML format, and templates, use testing-agentforce skill. That skill owns all testing content. For each coverage target, write one or more test scenarios: user utterance, expected topic routing, expected action invocations, and expected agent response. Include both happy paths and edge cases.
3. Write test spec YAML — Use template and reference files from testing-agentforce skill. Save to specs/<Agent_API_Name>-testSpec.yaml in SFDX project.
4. Create test metadata — Generate AiEvaluationDefinition from test spec using CLI.
5. Deploy test — Deploy AiEvaluationDefinition to org.
6. Run tests — Execute test run using CLI. Capture results.
7. Analyze results — Compare actual outcomes against expectations. For failures, identify whether issue is in agent code, backing logic, or test spec itself.
8. Iterate — Fix agent code or test spec as needed, redeploy, and re-run until coverage targets are met.

Reference Files

1. CLI for Agents — exact command syntax for test create, test run, test results
2. Core Language — agent structure for designing meaningful tests
3. Design & Agent Spec — Agent Spec as test coverage baseline
4. testing-agentforce skill — test spec YAML format, expectations, metrics, test design methodology, and test spec template

The Agent Spec

Agent Spec is the central artifact this skill produces and consumes. A structured design document representing agent's purpose, topic graph, actions with backing logic, variables, gating logic, and behavioral intent.

Agent Specs evolve with the agent. Sparse during agent creation (purpose, topics, directional notes). Fleshed out during agent build (flowchart, backing logic mapped, gating documented). Reverse-engineered when comprehending existing agents. Critical for advanced troubleshooting, providing reference to compare expected vs. actual behavior. During testing, test coverage maps against it.

Always produce or update Agent Spec as first step of any operation that changes or analyzes agent. It is consistent grounding to work from, and a durable artifact a developer can review.

Read Design & Agent Spec for Agent Spec structure and production methodology.

Assets

The assets/ directory contains templates and examples. Read when you need a starting point or a concrete reference for artifacts and source files.

• assets/agent-spec-template.md — Agent Spec template with all sections and placeholder content. Copy to <AgentName>-AgentSpec.md in project directory, then fill in during design. Save Agent Spec as file — significant design artifact that benefits from proper rendering, especially Mermaid Topic Map diagram.

• assets/local-info-agent-annotated.agent — Complete annotated example based on Local Info Agent, showing all major Agent Script constructs in context with inline comments explaining why each construct is used. Read when you need concrete reference for how concepts compose into working agent, or as fallback when focused examples in reference files aren't sufficient.

• assets/template-single-topic.agent — Minimal agent with one topic. Copy and modify for simple agents.

• assets/template-multi-topic.agent — Minimal agent with multiple topics and transitions. Copy and modify for complex agents.

• assets/invocable-apex-template.cls — Reference for invocable Apex classes. Copy and modify when complex Apex backing logic is desired.

Important Constraints

• Use only Salesforce CLI and Salesforce org. Do not reference or depend on other skills, MCP servers, or external tooling. All commands use sf (Salesforce CLI).

• Only certain backing logic types are valid for actions. For example, only invocable Apex (not arbitrary Apex classes) can back action. Similar constraints may apply to Flows and Prompt Templates. When wiring actions to backing logic, consult Design & Agent Spec reference file for valid types and stubbing methodology.

• sf agent generate test-spec is not for agentic use. It is interactive, REPL-style command designed for humans. When creating test specs, start from boilerplate template in assets instead.

Common Issues Quick Reference

Internal Error, try again later during publish: Invalid or missing default_agent_user. Re-run query from Design & Agent Spec, Section 3. Do not invent username.

Unable to access Salesforce Agent APIs... during preview: default_agent_user lacks permissions. See Agent User Setup & Permissions. Do NOT publish as fix — --use-live-actions does not require published agent.

Permission error referencing different username than configured: Same fix as above — error references org's default running user, but root cause is Einstein Agent User permissions.

Agent fails with permission error even though current topic's actions work: Planner validates ALL actions across ALL topics at startup. One missing permission fails entire agent.

Apex action returns empty results in live preview but works in simulated: WITH USER_MODE + missing object permissions = silent failure (0 rows, no error). See Agent User Setup & Permissions, Section 6.2.

Syntax Quick Reference

• Block order: system: → config: → variables: → connection: → knowledge: → language: → start_agent topic_selector: → topic: blocks
• Indentation: 4 spaces per indent level. Never use tabs. Mixing spaces and tabs breaks the parser.
• Booleans: True/False (capitalized)
• Strings: always double-quoted
• Numeric action I/O: bare number works for variables but fails at publish in action I/O. Use object + complex_data_type_name for numeric action parameters. See Complex Data Types for the full decision tree.
• after_reasoning: has NO instructions: wrapper
• No else if — use compound if x and y: or sequential flat ifs
• Reserved @InvocableVariable names: model, description, label — cannot be used as Apex parameter names
• @inputs and @outputs are ephemeral: @inputs only in with; @outputs only in set/if immediately after the action. @inputs in set = silent failure.

See Complex Data Types for the full Lightning type mapping decision tree. See Instruction Resolution for the 3-phase runtime model.

Architecture Patterns

Three primary FSM patterns. Full details with code in Architecture Patterns.

• Hub-and-Spoke (most common): start_agent routes to specialized topics. Each topic has "back to hub" transition. Do NOT create a separate routing topic.
• Verification Gate: Identity verification before protected topics. available when guards on protected transitions.
• Post-Action Loop: Post-action checks at TOP of instructions: -> trigger on re-resolution after action completes.

Scoring Rubric

Score every generated agent on 100 points across 7 categories: Structure (15), Safety (15), Deterministic Logic (20), Instruction Resolution (20), FSM Architecture (10), Action Configuration (10), Deployment Readiness (10).

See Scoring Rubric for the complete rubric.

Review Mode

When user provides an existing .agent file (e.g., review path/to/file.agent):

1. Read the file
2. Score against the 100-point rubric
3. List every issue grouped by category
4. Provide corrected code snippets
5. Offer to apply fixes

Safety Review

7-category LLM-driven safety review for .agent files. Integrated into Phase 0 of authoring and deployment. Categories: Identity & Transparency, User Safety, Data Handling, Content Safety, Fairness, Deception, Scope & Boundaries.

See Safety Review for the complete framework, severity levels, false positive guidance, and adversarial test prompts.

Discover & Scaffold

Validate action targets exist in org and generate stubs for missing ones.

See Discover Reference and Scaffold Reference.

CRITICAL: Stubs must return realistic data, not 'TODO'. Placeholder responses cause SMALL_TALK grounding because the LLM falls back to training data.

Deploy Lifecycle

Validate → deploy metadata → publish bundle → activate. See Deploy Reference for phases, error recovery, CI/CD, and rollback.

Template Assets

Ready-to-use .agent templates in assets/agents/ (hello-world, simple-qa, multi-topic, production-faq, order-service, verification-gate). See also assets/patterns/ for 11+ reusable design patterns and Examples for inline walkthroughs.

Additional References

Topic	File
Architecture patterns	architecture-patterns.md
Type mapping decision tree	complex-data-types.md
Feature validity by context	feature-validity.md
Instruction resolution model	instruction-resolution.md
Complete agent examples	examples.md