afv-library/plugins/builder/salesforce-development/skills/agentforce-generate/assets/patterns

Agent Script Patterns

This folder contains reusable patterns for common Agentforce scenarios.

Use references/patterns-by-requirement.md first to choose which pattern to apply for a given requirement. Then use this folder for concrete snippets.

Pattern Decision Tree

What do you need?
│
├─► Deterministic processing after a normal action result?
│   └─► Use: action-callbacks.agent
│       (run keyword for deterministic callbacks)
│
├─► Setup/cleanup for every reasoning turn?
│   └─► Use: lifecycle-events.agent
│       (before_reasoning / after_reasoning blocks)
│
├─► Navigate to specialist subagent and return with results?
│   └─► Use: bidirectional-routing.agent
│       (store return address, specialist subagent transitions back)
│
├─► Complex parameter passing to actions?
│   └─► Use: advanced-input-bindings.agent
│       (slot filling, variable binding, output chaining)
│
├─► Dynamic behavior based on user context?
│   └─► Use: system-instruction-overrides.agent
│       (tier-based, time-based, feature flag instructions)
│
├─► Several protected handlers share one authentication gate?
│   └─► Use: open-gate-routing.agent
│       (verified proof plus a temporary return address; no focus latch)
│
├─► Later external actions must wait for earlier successful results?
│   └─► Use: multi-step-workflow.agent
│       (action-result gates; no conversational step counter)
│
└─► None of the above?
    └─► Start with: ../agents/hello-world.agent

Patterns Overview

1. action-callbacks.agent

Purpose: Process a normal parent-action result deterministically with run.

Use when:

• A declared follow-up action must run after the parent returns normally
• The callback consumes exact parent outputs
• A returned negative result must be normalized without being presented as success

Do not use a callback as evidence that a parent action recovered from an execution failure. If the parent does not return normally, this pattern does not establish downstream completion.

Key syntax:

track_shipment: @actions.get_shipment_status
    with tracking_number=...
    run @actions.normalize_shipment_result
        with found=@outputs.found
        with status=@outputs.status

---

2. lifecycle-events.agent

Purpose: Run code once before/after the reasoning loop on each turn.

Use when:

• Refresh external context before each response
• Log analytics after each turn
• Apply setup or cleanup that genuinely must run on every reasoning turn

Key syntax:

subagent conversation:
   before_reasoning:
      run @actions.refresh_context

   reasoning:
      instructions: ->
         | Answer using the refreshed external context and surviving history.

   after_reasoning:
      run @actions.log_analytics

---

3. bidirectional-routing.agent

Purpose: Navigate to a specialist request handler and return with results.

Use when:

• Complex workflows spanning multiple subagents
• "Consult an expert" pattern
• A shared specialist must return to one of several possible callers
• Want separation of concerns

Key syntax:

# When the return target is fixed, use direct transitions and no state.
consult_pricing: @utils.transition to @subagent.pricing_specialist
return_with_results: @utils.transition to @subagent.main_hub

Persist a return address only when the same specialist has multiple possible callers and runtime logic must choose the correct one.

---

4. advanced-input-bindings.agent

Purpose: Master all parameter binding techniques for actions.

Use when:

• Learning different ways to pass values to actions
• Complex multi-input action scenarios
• Chaining outputs between multiple actions
• Mixing LLM slot filling with stored state

Key syntax:

reasoning:
   actions:
      # Slot filling: LLM extracts from conversation
      lookup: @actions.get_order
         with order_id=...

      # Variable binding: Use stored state
      bound: @actions.get_order
         with order_id=@variables.current_order_id

      # Output chaining: Use previous action's result
      process: @actions.create_order
         with items=...
         set @variables.order_id = @outputs.order_id
         run @actions.send_notification
            with order_id=@outputs.order_id    # Chained output

Binding Pattern Quick Reference:

Pattern	Syntax	When to Use
Slot Filling	with x=...	LLM extracts from conversation
Fixed Value	with x="value"	Always use a constant
Variable	with x=@variables.y	Use stored state
Output	with x=@outputs.y	Chain from previous action

---

5. system-instruction-overrides.agent

Purpose: Dynamic agent behavior based on context (user tier, time, features).

Use when:

• Different behavior for different user segments (VIP vs standard)
• Time-based changes (business hours vs after hours)
• Feature flags controlling agent personality
• A/B testing different conversation styles

Key syntax:

# System block: Static base instructions
system:
   instructions: "You are a professional agent. Be helpful and courteous."

# Subagent reasoning: Dynamic overrides
reasoning:
   instructions: ->
      if @variables.customer_tier == "vip":
         | PRIORITY CUSTOMER - Provide white-glove service.
         | You have authority to offer 20% discounts.

      if @variables.business_hours == False:
         | We are outside business hours.
         | Complex issues should be logged for follow-up.

      | Respond to the customer's inquiry.

Override Strategy:

Layer	Type	Best For
system:	Static	Guardrails, base personality
reasoning:	Dynamic	Personalization, context-aware behavior

---

6. open-gate-routing.agent

Purpose: Return a verified user to the correct protected request handler without locking conversational focus.

Use when:

• Multiple protected subagents require authentication before access
• Users should be redirected to auth, then automatically returned to their intended subagent
• Verification proof must deterministically guard protected actions
• Cancellation and changed intent must take effect on the next turn

Key syntax:

# Protected request handler records only its temporary return address.
if @variables.authenticated == False:
   set @variables.pending_destination = "protected_orders"
   transition to @subagent.authentication

# Successful verification returns deterministically and clears the address.
if @variables.authenticated == True and @variables.pending_destination == "protected_orders":
   set @variables.pending_destination = ""
   transition to @subagent.protected_orders

Credit: The original open-gate pattern was contributed by Hua Xu (Salesforce APAC FDE team) from the Kogan agent deployment. This version preserves the shared verification gate while replacing the conversation focus lock with fresh-intent routing and an expiring return address.

Do not add an open_gate, active_workflow, or similar focus latch merely to bypass reasoning. If only one protected handler exists, use a fixed transition and omit pending_destination too.

---

7. multi-step-workflow.agent

Purpose: Enforce ordering between external actions using their successful results as the source of truth.

Use when:

• A later action would be invalid or unsafe before an earlier external action
• A canonical ID returned by one action is required by later action bindings
• Failure must leave later actions unavailable

Do not use when:

• The "steps" are only questions in a conversation
• A current_step counter would duplicate per-action completion results
• The goal is to keep a request handler focused despite a changed user intent

Key syntax:

save_profile: @actions.update_profile
   with customer_id=@variables.created_customer_id
   with phone=...
   set @variables.profile_saved = @outputs.success

save_preferences: @actions.set_preferences
   with customer_id=@variables.created_customer_id
   with notification_method=...
   available when @variables.profile_saved == True

---

Pattern Combinations

These patterns can be combined:

lifecycle-events + action-callbacks
├── before_reasoning: Initialize context
├── reasoning: Process with callbacks
│   └── action with run callbacks
└── after_reasoning: Log results

authentication-return + lifecycle-events
├── before_reasoning: Verify proof or refresh external context
├── reasoning: Protected actions only when proof is valid
└── after_reasoning: Clear a consumed return address and log the result

Validation and Review Guidance

Patterns are not scored by quantity. More patterns do not mean a better agent.

Use a pattern only when a requirement justifies it. Review quality should focus on:

• Requirement fit (pattern solves a real need)
• Correct implementation syntax
• Behavioral reliability in preview traces
• Maintainability (no unnecessary complexity)

Pattern	What reviewers should verify
Action Callbacks	run chains are needed and not nested incorrectly
Lifecycle Events	before_reasoning/after_reasoning are used intentionally, not as default noise
Bidirectional	Return path is explicit only when workflow requires return
Input Bindings	Pinning vs ... matches posture and data trust requirements
System Overrides	Dynamic instructions are requirement-driven and not contradictory
Authentication Return	Proof comes from verification; return address is cleared; cancel and sign-out work
Multi-Step Workflow	Each flag is a trusted action result; failure cannot unlock the next action

Anti-Patterns to Avoid

❌ Don't	✅ Do Instead
Nested run inside run	Sequential run at same level
Lifecycle in wrong order	before_reasoning, reasoning, after_reasoning
Store a fixed return target	Use direct transitions in both directions
Use lifecycle for one-time setup	Initialize outside the per-turn lifecycle
Add current_step plus completion flags	Derive availability from successful external results
Store user input already present in history	Bind safe user inputs with ...
Add a focus latch for cheaper routing	Route the latest intent; reproduce a failure before adding deterministic control
Leave authentication without an exit	Clear pending return state on cancel and proof state on sign-out