Agent Script Tutorial: Build Your First Agentforce Agent, Step by Step.

I get some version of the same message every time I post about Agent Script: "okay, I get what the blocks *are* — config, subagent, actions — but how do I actually go from a blank file to something that works?" Fair question. My last post on this broke down the anatomy of a script. This one is the thing I wish existed when I opened my first blank .agent file and just stared at it for ten minutes.

We're building a real agent here — an internal IT Helpdesk assistant that resets passwords and files tickets — starting dead simple and adding complexity one block at a time. By the end you'll have a working two-subagent script and, more importantly, you'll know exactly what to Google when yours breaks.

TL;DR: We build an IT Helpdesk agent from a blank file — one subagent that handles password resets with identity verification, a second that escalates to a human when things get messy. Along the way you'll see every core block in context (not just defined in a table), plus the beginner mistakes that eat the most time and how to actually validate a script before you publish it.

Before You Open VS Code

You don't need much, but you do need these three things or you'll be debugging the wrong problem:

• An Agentforce-enabled org (a Developer Edition org works fine for this)
• A Salesforce DX project with Agentforce DX installed in VS Code
• An authoring bundle generated for a new agent — retrieve or create one and you'll get a file at force-app/main/default/aiAuthoringBundles/<Agent_API_Name>/<Agent_API_Name>.agent

That .agent file is a blank canvas the moment it's created. Let's fill it in.

Step 1: The Skeleton Every Agent Needs

Before any logic, an agent needs to know who it is. That's the config and system blocks — and honestly, this part trips up more beginners than the actual logic does later, because it's tempting to skip straight to the "interesting" part.

config:
  developer_name: "IT_Helpdesk_Agent"
  agent_label: "IT Helpdesk Agent"
  agent_type: "AgentforceServiceAgent"
  default_agent_user: "helpdesk_agent_user@yourorg.com"
  description: "Helps employees reset passwords and file IT tickets, escalating anything sensitive to a 
  human technician."

system:
  welcome: "Hi, I'm the IT Helpdesk assistant. Are you here about a password reset or something else?"
  error: "Something went wrong on my end — let me get a technician to help you directly."

Two things worth internalizing here, because they'll save you later:

• default_agent_user is not decorative. It's the actual Salesforce user whose permissions the agent runs with. If that user can't see a field or object, neither can your agent — no matter what your Apex action says.
• error isn't a fallback you'll never see. You will see it. Write it like a real message a stressed-out employee would actually want to read, not "An error has occurred."

Step 2: Give It Memory — Variables

Right now the agent has a personality but no memory. It can't track anything across the conversation yet — like whether the employee has actually verified who they are. That's what variables is for.

variables:
  is_verified: boolean
  employee_id: string
  reset_attempts: number

Think of these as the agent's short-term memory for this one conversation. Anywhere else in the script, you reference them as @variables.is_verified. This matters more than it sounds like it should — it's the difference between an agent that "remembers" verification happened versus one that re-asks the same question because it forgot three messages ago.

Step 3: Your First Subagent — Password Reset

This is where the actual behavior lives. A subagent is a self-contained chunk of logic — its own description, its own reasoning, its own available actions.

subagent password_reset:
  description: "Verifies employee identity and resets their Salesforce password."

  reasoning:
    instructions:
      "Ask for the employee's work email if not already provided ->
       If @variables.is_verified is false | Send a verification code and ask the
       employee to read it back before doing anything else.
       If @variables.reset_attempts > 2 | Stop and hand off to a human technician,
       do not attempt another verification code.
       Otherwise | Reset the password using the reset action and confirm
       completion in plain language."
    actions:
      - verify_employee_identity
      - reset_password

  actions:
    verify_employee_identity:
      description: "Sends a verification code to the employee's registered email and checks it against their input."
      target: apex://VerifyEmployeeIdentityAction

    reset_password:
      description: "Resets the employee's Salesforce password once identity is verified."
      target: apex://ResetPasswordAction

Read the reasoning.instructions block slowly, because it's doing two different jobs at once and beginners usually only notice one of them:

• The part before the -> is natural language — the LLM interprets it, same as a prompt.
• The If @variables.x lines after it are hard conditionals, evaluated against real variable values. The model isn't "deciding" whether reset_attempts is greater than 2 — it's a fact, checked deterministically.

That distinction is the entire reason Agent Script exists instead of just writing a longer prompt. A prompt can be talked out of a rule. A conditional can't.

Step 4: The Entry Point — start_agent

Right now you've got a subagent that works, but nothing tells the conversation to start there. Every script needs exactly one entry point:

start_agent agent_router:
  label: "Helpdesk Router"
  description: "Determines whether the employee needs a password reset or general IT support."

  reasoning:
    instructions:
      "If the employee mentions a password, login, or being locked out ->
       hand off to Password_Reset_Agent.
       Otherwise | ask a clarifying question about what kind of IT issue they're having."

  actions:
    go_to_password_reset: @utils.transition to @subagent.password_reset

Forget this block and your agent will validate fine but do absolutely nothing useful when a real conversation hits it — there's no door for the conversation to walk through. This is, without exaggeration, the single most common "why isn't my agent doing anything" issue I see from people just starting out.

Step 5: Add a Second Subagent — Escalation

Now let's make it feel like a real production agent instead of a demo. Three failed verification attempts shouldn't just dead-end — it should hand off to a human, cleanly.

subagent escalation:
  description: "Hands the conversation to a human IT technician when automated verification fails or the issue is
  out of scope."

  reasoning:
    instructions:
      "Apologize once, briefly — don't over-explain the failure.
       Confirm the employee's issue in one sentence so the technician has context.
       Transition to a live agent immediately after that confirmation."
    actions:
      - transition_to_human

  actions:
    transition_to_human:
      description: "Hands off the conversation to a human technician queue."
      target: flow://EscalateToHumanTechnician

Notice this action points at flow:// instead of apex://. That's deliberate, not a typo — Agent Script doesn't care whether the actual work happens in Apex or Flow, it just needs a target it can invoke. Use whichever your team already has, or whichever is easier for someone else to maintain after you.

How a Message Actually Moves Through This

This is the mental model that made everything click for me — worth having open in a tab while you're still getting a feel for how these blocks talk to each other.

Beginner Mistakes That Waste the Most Time

I've made every one of these myself, and I've watched other people make them too. Save yourself the twenty minutes of confused staring:

What Goes Wrong	Why It Happens	Fix
Agent validates fine but never responds	No start_agent block, so there's no entry point for the conversation	Every script needs exactly one start_agent
Agent keeps re-asking a question it already got answered	Answer was never stored in a variable — the model has no memory of it	Capture it in variables and reference with @variables.name
"Never do X" instruction gets ignored occasionally	Rule was written as prose in reasoning.instructions instead of an explicit conditional	Move hard rules into If @variables.x conditionals, not just sentences
Validation fails with no obvious reason	Usually a missing colon, wrong indentation, or a typo in a block name	Run AFDX: Validate This Agent after every small change, not just before publishing
Action never fires	The action's target doesn't match the real Apex method's @InvocableMethod name, or it's not listed under the subagent's actions	Double-check the target path and that it's referenced in both places
Confusing "topic" vs "subagent" in docs or old code	Salesforce renamed topics to subagents — same concept, different word depending on version	Just know they mean the same thing; don't rewrite working scripts over naming alone

Practices Worth Building Now, Not Later

• Validate constantly, not just at the end. Run AFDX: Validate This Agent after every meaningful edit. Catching a typo in a 10-line script takes ten seconds; catching it in a 200-line script takes ten minutes.
• Put hard rules in conditionals, not prose. If something must never happen — skipping verification, exceeding a retry limit — express it as an explicit If @variables.x check. Anything left as a sentence is a suggestion, not a rule.
• Name variables like you'll forget what they mean in six months. Because you will. reset_attempts beats x every time.
• Keep each subagent doing one job. The moment a single subagent's reasoning block starts handling three unrelated situations, split it. It's the same instinct as keeping an Apex method focused — easier to read, easier to test, easier to hand to someone else.
• Preview before you publish. Test the actual conversation flow from the script file itself. Reading the logic and watching it run are two very different confidence levels.
• Commit it like real code, because it is real code. The whole point of Agent Script is that it lives in your DX project — treat it with the same code review standards as an Apex class, not as a config file nobody looks at twice.

Where to Go From Here

You've now got a two-subagent agent with memory, a real conditional rule, and a clean human escalation path — which honestly puts you past where a lot of "finished" demo agents stop. The natural next step is chaining in a connected_subagent to hand off to a completely separate Agentforce agent, or grounding a subagent's answers in real data with RAG instead of a single Apex lookup. Both are just more of the same building blocks, arranged with a bit more intention.

Building your own agent right now and stuck on something specific? Drop it in the comments — I read every one, and "my conditional isn't firing" is usually a five-minute fix once I can see the actual script.

 

Agent Script for Developers: Coding Agentforce Agents Like Real Software

Building an agent by clicking through Agentforce Builder works fine until your logic gets specific — "offer free shipping only if the order total is over $100 AND the customer is a loyalty member AND it's not already on backorder." At that point, natural-language instructions to an LLM start to feel like duct tape. Agent Script is Salesforce's answer: a real, readable scripting language purpose-built for agents.

TL;DR: Agent Script is a declarative, human-readable language for defining Agentforce agents — their subagents (formerly called topics), instructions, variables, and actions — as code instead of only as clicks. It blends natural-language reasoning instructions with deterministic if/else logic, lives in a .agent file inside your Salesforce DX project, and is fully supported in VS Code with syntax highlighting and validation. If you've ever wished you could put an agent under version control, this is how.

Why Developers Should Care

Agentforce Builder's canvas view is genuinely good for admins — natural language in, working agent out. But every agent eventually needs the same things any serious codebase needs: predictable branching logic, reusable structure, code review, and a diff you can actually read. Agent Script gives you all of that because, under the hood, every agent you build in Agentforce — whether through chat, canvas, or script — is Agent Script. The Script view just lets you work with it directly instead of through a UI abstraction.

That matters for a very practical reason: it puts agent definitions in your Salesforce DX project, next to your Apex and LWC, where they can be versioned, code-reviewed, and deployed the same way as everything else you ship.

The Building Blocks of a Script File

An Agent Script file is organized into a small number of named blocks. Once you recognize them, most scripts read top to bottom without much translation:

Block	What it holds
config	Core agent settings — developer_name, agent_label, description, agent_type, and which Salesforce user the agent runs as.
system	Agent-wide instructions and required messages like welcome and error.
variables	Named state the agent tracks across a conversation, referenced anywhere as @variables.<name>.
subagent	A self-contained unit of behavior — its own description, instructions, and available actions. This is where most of the actual logic lives.
start_agent	The entry point every conversation begins at; decides which subagent should handle the user's request.
connected_subagent	A reference to a different Agentforce agent in your org, so one agent can delegate work to another.

If "subagent" sounds like a rename, it is — Salesforce renamed topics to subagents in April 2026 with no functional change, so don't be surprised if you see both terms depending on which doc or org version you're looking at.

A Worked Example: Order Status, in Script

Let's script a small piece of the same order-status scenario from our last post — but this time controlling when the agent should hand off to a human instead of just answering.

config:
  developer_name: "Order_Support_Agent"
  agent_label: "Order Support Agent"
  agent_type: "AgentforceServiceAgent"
  default_agent_user: "order_support_agent_user@yourorg.com"
  description: "Helps customers check order status and escalates delayed orders to a human agent."

system:
  welcome: "Hi! I can help you check on an order — what's your order number?"
  error: "Something went wrong on my end. Let me connect you with a teammate."

variables:
  order_status: string
  days_delayed: number

subagent order_lookup:
  description: "Looks up an order's status and delivery estimate when the customer provides an order number."

  reasoning:
    instructions:
      "Ask for the order number if it hasn't been provided ->
       If @variables.days_delayed > 3 | Apologize for the delay before sharing status details.
       Otherwise | Share the order status plainly and offer to help with anything else."
    actions:
      - get_order_status
      - transition_to_escalation

  actions:
    get_order_status:
      description: "Calls Apex to retrieve status, delivery estimate, and delay in days for an order number."
      target: apex://OrderStatusAction

subagent escalation:
  description: "Hands off to a human agent when a delay is significant or the customer asks for a person."
  reasoning:
    instructions:
      "If @variables.days_delayed > 7 | Explain that a specialist will follow up and transition immediately.
       Otherwise | Ask one clarifying question before deciding whether to escalate."

A few things worth noticing:

• The line with -> inside reasoning.instructions is where Agent Script earns its keep. Everything before it can be plain natural language; everything after can be a hard conditional evaluated against a real variable — not something the LLM has to infer from conversation history.
• get_order_status here points at apex://OrderStatusAction — the exact custom Apex action with @InvocableMethod we built in the previous post. Agent Script doesn't replace Apex actions; it's the orchestration layer that decides when and whether to call them.
• Variables like days_delayed give the agent reliable memory instead of leaning on the LLM to remember and recompute values mid-conversation.

Three Ways to Write It (All Produce the Same Thing)

Salesforce is intentionally flexible about how you author a script:

1. Chat with Agentforce and describe what you want ("if the order's more than a week late, hand it straight to a human") — Agentforce converts that into subagents, actions, and instructions for you.
2. Canvas view — a visual, block-based editor where / inserts logic patterns like if/else and @ inserts references to subagents, actions, or variables.
3. Script view — write and edit the raw .agent file directly, with the same syntax highlighting and autocomplete you'd expect from any language extension in VS Code.

All three are the same underlying artifact. You can start in canvas view and drop into script view the moment the logic gets too specific for clicking — and back again.

Working in VS Code with Agentforce DX

If you'd rather live in your editor than in Setup, Agentforce DX brings the whole workflow local:

1. Generate or retrieve an authoring bundle for your agent into your DX project — it lands at force-app/main/default/aiAuthoringBundles/<Agent_API_Name>/<Agent_API_Name>.agent.
2. Edit the .agent file directly, or open the Agentforce Vibes panel to describe changes in natural language and let it edit the script for you.
3. Validate the file before you publish — VS Code's AFDX: Validate This Agent command (or the equivalent CLI command) checks that the script compiles and flags syntax errors with their exact location.
4. Preview the agent from the script file itself to test behavior before publishing it back to your org.

One habit worth building early: validate often, not just before a deploy. Agent Script errors are usually small — a missing colon, a typo in a block name — and they're far easier to fix one at a time than after you've written fifty lines on top of a broken block.

Where This Fits with What You Already Know

If you've spent time in Flow, a lot of this will feel familiar wearing different clothes: subagents are a bit like Flow's screen-by-screen structure, reasoning instructions are your decision logic, and actions are the same invocable Apex, Flow, and prompt-template building blocks Agentforce already supports. The real shift is that it's all expressed as one readable, versionable file instead of a set of linked records you navigate by clicking.

The Bottom Line

Agent Script doesn't replace Agentforce Builder — it's what Agentforce Builder is writing on your behalf every time you build an agent through chat or canvas. Once your agent's logic outgrows what feels safe to leave entirely to LLM interpretation, dropping into Script view (or straight into VS Code with Agentforce DX) gives you the same rigor you already expect from Apex: version control, code review, and behavior you can actually predict.

Have you tried writing Agent Script directly, or are you still building through canvas view? Let me know how it's going in the comments below.

Agentforce for Developers: Building Your First Custom Action with Apex

Everyone's talking about Agentforce from the admin side — click-based agent setup, prompt templates, topics and instructions. But if you're an Apex developer, the real question is different: how do I plug my own logic into an agent so it can actually do something in my org, not just talk about it?

TL;DR: Agentforce agents don't just answer questions — they take action by calling "actions" under the hood. The fastest way to give an agent real capability is an Apex class with an @InvocableMethod. Write the method, add a couple of annotations Agentforce reads to understand what your code does, deploy it, wire it up in Agentforce Builder, and your agent can now query, update, or trigger anything Apex can reach.

Why This Matters for Developers Right Now

Admins can build a lot with Agentforce out of the box — Flow actions, prompt templates, standard actions for common objects. But the moment a business process needs custom validation, a call to an external system, or logic too complex for a Flow, the agent needs your code. That's the developer's entry point into Agentforce, and it's honestly one of the most in-demand skills in the ecosystem right now — most teams have admins who can configure an agent, but far fewer have developers who know how to extend one safely.

The good news: if you already know how to write an invocable Apex method for Flow, you're 80% of the way there. Agentforce reuses the same annotation.

How an Agent Actually Calls Your Code

It helps to think of an agent as a planner, not a black box. When a user asks it something, the agent:

1. Reads the instructions and topics it's been configured with.
2. Decides which action (or actions) can help complete the request.
3. Calls that action with whatever inputs it can infer from the conversation.
4. Reads the output and decides what to say or do next.

An "action" can be a Flow, a prompt template, a REST-exposed Apex method, or — what we're building today — an Apex class annotated with @InvocableMethod. The annotation is what makes your class show up as a selectable action inside Agentforce Builder.

What We're Building

A support agent for a fictional subscription business needs to answer: "What's the status of order 00012345?" Instead of guessing, it should call real Apex that queries the actual record. Here's the class:

public with sharing class OrderStatusAction {

    @InvocableMethod(
        label='Get Order Status'
        description='Returns the current status, expected delivery date, and total for a given order number.'
    )
    public static List<OrderStatusResult> getOrderStatus(List<OrderStatusRequest> requests) {
        List<OrderStatusResult> results = new List<OrderStatusResult>();

        for (OrderStatusRequest req : requests) {
            OrderStatusResult result = new OrderStatusResult();

            Order__c ord = [
                SELECT Status__c, Expected_Delivery__c, Total_Amount__c
                FROM Order__c
                WHERE Order_Number__c = :req.orderNumber
                WITH USER_MODE
                LIMIT 1
            ];

            result.status = ord.Status__c;
            result.expectedDelivery = ord.Expected_Delivery__c;
            result.totalAmount = ord.Total_Amount__c;
            results.add(result);
        }

        return results;
    }

    public class OrderStatusRequest {
        @InvocableVariable(label='Order Number' description='The order number the customer
 is asking about, e.g. 00012345.' required=true)
        public String orderNumber;
    }

    public class OrderStatusResult {
        @InvocableVariable(label='Status' description='Current fulfillment status of the order.')
        public String status;

        @InvocableVariable(label='Expected Delivery' description='The date the order is expected to arrive.')
        public Date expectedDelivery;

        @InvocableVariable(label='Total Amount' description='The total charged for this order.')
        public Decimal totalAmount;
    }
}

A few details that matter more here than in ordinary Apex:

• The description on @InvocableMethod and every @InvocableVariable isn't a comment — it's instructions the agent reads at run time to decide when to call this action and how to fill in its inputs. Vague descriptions produce agents that call the wrong action or leave inputs blank.
• WITH USER_MODE enforces the running user's field- and object-level security, which matters even more with an agent in the loop, since you don't want it surfacing data the requesting user couldn't normally see.
• Keep request/response wrapper classes flat and simple. Agents reason better over a handful of clearly-labeled fields than a deeply nested structure.

Wiring the Action into Agentforce Builder

Once the class is deployed:

1. From Setup, open Agentforce Studio (or Agent Studio, depending on your org's release) and open the agent you want to extend, then start a new version.
2. Under Actions, choose New Action, then set the reference type to Apex and the category to Invocable Method.
3. Select OrderStatusAction from the list — Agentforce Builder will pre-fill the action's name, description, inputs, and outputs directly from your annotations.
4. Check Show in conversation on any output field you want the agent to surface directly in its reply (for example, status), rather than just reasoning over silently.
5. Save, commit the version, and activate it.

Testing Before You Ship It

Don't skip this step. Agentforce Builder has a Preview / Live Test Mode pane where you can chat with the draft agent before it goes live. Ask it the exact question a real user would ask — "Where's my order 00012345?" — and confirm two things:

• It actually selects your action instead of hallucinating an answer.
• The output it reads back matches what your Apex actually returned.

If it doesn't call the action reliably, the fix is almost always a clearer description, not more code. Agents lean heavily on that text to route correctly.

Permission Set Gotcha

This trips up a lot of developers coming from a pure-Apex background: an agent runs with the permissions of whatever user or agent user is assigned, not with elevated access. If the Apex class, the custom object, or the specific fields in OrderStatusAction aren't granted through a permission set assigned to that user, the action will silently fail to appear as usable — even though it's deployed and wired up correctly in Builder. If your action isn't behaving, check permissions before you touch the code.

From One Action to a Real Agent Skill

A single action is a good first step, but the pattern scales:

• Chain actions together — one action to look up the order, another to check refund eligibility, another to actually issue the refund — and let the agent sequence them based on the conversation.
• Use External Objects with Prompt Builder to ground the agent's generated responses in live data from systems outside Salesforce, instead of custom Apex callouts for every case.
• Log agent actions back to Data Cloud so you can monitor which actions get called, how often they succeed, and where agents get stuck — treat it like observability for a new kind of user.

The Bottom Line

If you can write an invocable Apex method for a Flow, you already have the core skill Agentforce needs from a developer. The shift isn't really technical — it's about writing your method and field descriptions for an AI reader instead of a human one, and being deliberate about what the agent is and isn't allowed to touch. Start with one well-scoped action, test it in Live Test Mode until it behaves predictably, and expand from there.

Have you built a custom Agentforce action yet? Share what it does — or where it broke — in the comments below.