Agent Script DSL
Use this skill when the work involves authoring, editing, validating, or deploying Agentforce agent definitions through source-controlled metadata rather than through the Agentforce Builder UI. This skill covers the YAML-based .agent file format, the three composite metadata types that make up an agent (GenAiPlugin, GenAiPlanner/GenAiPlannerBundle, BotVersion), LSP validation tooling, the sf agent test run CLI command, and the metadata deployment lifecycle end-to-end. It does not cover Apex implementation of invocable agent actions (use agentforce/custom-agent-actions-apex) and does not cover UI-driven agent creation workflow (use agentforce/agentforce-agent-creation).
Agentforce agent definitions are stored as YAML-based declarative metadata in .agent files within a Salesforce DX project. These files are the machine-readable representations of the same configuration surfaced in Agentforce Builder. Understanding the relationship between the three composite metadata types — GenAiPlugin, GenAiPlanner (or GenAiPlannerBundle from API v64+), and BotVersion — is essential for correct deployment and source control management.
Before Starting
Gather this context before working on anything in this domain:
- What is the target Salesforce CLI and API version? GenAiPlannerBundle was introduced at API v64 (Spring '26). Projects targeting API v60–v63 use GenAiPlanner instead. Mixing versions in the same project causes deploy failures.
- Is the Salesforce Extensions for VS Code installed along with the Agentforce extension? LSP-based DSL validation requires both. Without them, YAML errors in
.agent files produce silent or cryptic deploy failures.
- Is the project structured as a Salesforce DX project with
sfdx-project.json? The sf agent CLI commands operate on DX-structured projects only.
- What is the current state of the agent in the org? Retrieving before editing is critical — manually authored
.agent files that diverge from the deployed BotVersion state produce merge conflicts on the next retrieve.
- Are there existing GenAiPlugin or GenAiFunction metadata files in the project? These must be co-deployed with BotVersion and GenAiPlannerBundle as a unit.
Core Concepts
The Three Composite Metadata Types
An Agentforce agent in source control is not a single file. It is composed of three linked metadata types that must be deployed together as a bundle:
GenAiPlugin — the action definition layer. Each GenAiPlugin corresponds to a topic in Agentforce Builder. It declares the topic label, description (used by the LLM for topic classification), and references to the GenAiFunction records (actions) that belong to the topic. A plugin that has a vague or overlapping description will cause the LLM planner to route incorrectly, even if the metadata deploys cleanly.
GenAiPlanner / GenAiPlannerBundle — the orchestration configuration layer. GenAiPlanner (API v60–v63) or GenAiPlannerBundle (API v64+) configures the LLM reasoning engine: model selection, planner instructions (the system prompt), and the set of GenAiPlugins the agent can invoke. The bundle variant introduces the ability to link the planner to a BotVersion within a single metadata record, replacing the manual cross-reference approach of the earlier type.
BotVersion — the conversation container and channel routing shell. BotVersion wraps the Bot record and manages conversation session parameters, language settings, and fallback behavior. It also carries the reference that links a deployed agent to the GenAiPlannerBundle. BotVersion without a linked planner produces a legacy Einstein Bot, not an Agentforce agent. The presence of the genAiPlannerBundle element in BotVersion XML is the authoritative indicator that a BotVersion is an Agentforce agent and not a legacy scripted bot.
All three types must be retrieved and deployed as a coherent unit. Partial deploys — for example, deploying only GenAiPlugin changes without an updated BotVersion — will either fail validation or produce a deployed state that diverges from the source of truth in version control.
YAML-Based .agent File Format
The .agent file is the primary human-authoring surface for Agentforce agent definitions in a DX project. It is a YAML file that declaratively defines the full agent configuration: agent metadata, planner instructions (the system prompt persona), topic references, and action references. The Salesforce CLI tooling (sf agent) consumes .agent files and translates them into the correct GenAiPlugin/GenAiPlanner/BotVersion metadata records during deployment.
Key YAML keys in a typical .agent file:
name — the agent API name (immutable after first deployment)type — agent for standard Agentforce agentsspec.agentType — differentiates service agents from custom agentsspec.description — the agent's role description (becomes part of the system context)spec.topics — array of topic definitions, each with a label, description, and list of actionsspec.plannerInstructions — the full system prompt block governing persona, tone, and constraints
The Salesforce Agentforce VS Code extension provides LSP validation against the .agent schema. Hover hints, inline error diagnostics, and autocomplete are available when the extension is active. Authoring .agent files without LSP validation significantly increases the risk of silent schema violations that only surface at deploy time.
Agent Script — the Language for the New Agentforce Builder
Agent Script is the authoring language for the new Agentforce Builder. Salesforce describes it as "the language for building agents in Agentforce Builder" that "combines the flexibility of natural language instructions for handling conversational tasks with the reliability of programmatic expressions for handling business rules." It is a distinct, higher-level surface than the raw .agent/GenAiPlugin metadata described above: Agent Script is what an author writes, and the platform compiles it down into the lower-level runtime metadata.
Compiled, not interpreted. Agent Script is a compiled language. Per the docs, "When you save a version of the agent, the script compiles into lower-level metadata that is used by the reasoning engine." The consequence for source control: the script is the source of truth, and the compiled metadata is a build artifact. Editing the compiled metadata directly rather than the script is the same anti-pattern as hand-editing generated output.
Hybrid reasoning — two instruction types. Agent Script "combines deterministic logic with LLM reasoning in a single workflow. This hybrid approach gives you predictable execution where you need it, while preserving the LLM's ability to handle nuanced conversations." There are two instruction types:
- Logic instructions use
-> and "run deterministically every time. Use them for business rules, running actions, setting variables, and conditional branching." These are the parts of an agent whose behavior you can guarantee.
- Prompt instructions use
| and contain "natural language sent to the LLM. The LLM interprets these instructions and decides how to respond to the customer." These are the parts you cannot fully guarantee — the same routing-quality concern that applies to topic descriptions (see below) applies here.
Deciding which behavior belongs in a -> logic instruction (deterministic: compliance checks, entitlement lookups, variable math, branching) versus a | prompt instruction (conversational: greeting, summarizing, empathetic handling) is the core design skill for Agent Script, and it is where hybrid reasoning earns its reliability.
Whitespace-sensitive. Agent Script "is whitespace-sensitive, similar to languages like Python or YAML, meaning that indentation is used to indicate structure and relationships between properties." Mixed tabs/spaces and inconsistent indentation are structural errors, not cosmetic ones.
@-prefixed resource references. Resources are accessed using the @ symbol. The documented patterns are @actions.<action_name>, @subagent.<subagent_name>, @variables.<variable_name>, and @outputs.<output_name>. A set of built-in utilities is exposed under @utils, including @utils.escalate (escalate to a human service rep), @utils.setVariables (instruct the LLM to set variable values), and @utils.transition to (transition to a different subagent).
Top-level statement set. The reference documents these top-level blocks: config (agent configuration), connection (external connections such as Enhanced Chat), language (supported languages), system (agent instructions and messages), variables (global agent variables), subagent (a subagent's instructions and actions), start_agent (entry point for subagent classification and routing), reasoning (instructions and tools for the LLM), and after_reasoning (an optional block inside a subagent that runs after the reasoning loop exits).
Control flow and action invocation. Conditional branching uses if / else (for example, if @variables.is_member == True:), with comparison operators including ==, !=, <, >, and is None / is not None. Actions are invoked with run (execute an action deterministically), set (store a value in a variable), and with (bind an input parameter).
Terminology — topics are now subagents. Per the reference, "Beginning in April 2026, agent topics are now called subagents. There are no changes to functionality." When reconciling older .agent metadata (which uses topic terminology and GenAiPlugin-per-topic) against newer Agent Script authored in the Builder, treat "topic" and "subagent" as the same concept; the routing-quality concern is unchanged by the rename.
Three authoring surfaces in the new Builder. The new Agentforce Builder exposes Agent Script through three views, and choosing the right one is part of the workflow:
- AI Assistance — chat with Agentforce and describe what you want the agent to do; the system converts the request into structured components. Best for a first draft or bootstrapping an unfamiliar surface.
- Canvas — Agent Script is "summarized into easily understandable blocks, which you can expand to view the underlying script." Best for review and for non-authors reasoning about behavior.
- Script view — "Advanced users can switch to Script view to write and edit script directly, with developer-friendly aids like syntax highlighting, autocompletion, and validation." This is the surface that corresponds most directly to source-controlled authoring.
Maturity. Agent Script and the new Agentforce Builder are pre-GA. Salesforce's launch blog states the capability entered pilot in October 2025 and became public beta in November 2025 ("Agent Script is currently in pilot and will be available in November 2025 as public beta for all customers"), and help-article guidance continues to frame it as open beta ("Cross-over options will be considered closer to GA"). No GA date is stated in the official documentation reviewed. Because assertion schemas and syntax can shift pre-GA, pin your CLI plugin versions and revalidate after each release. The new Builder itself carries no additional cost: "No additional cost if you have purchased Agentforce. The new builder is available at no charge in Agentforce Studio."
Open-sourced toolchain. Salesforce open-sourced the Agent Script specification and its developer tooling — the parser (both a TypeScript parser and a Tree-sitter parser), an 18+-pass linter, the compiler (parsed AST → Salesforce runtime specification, with source-map support), and a Language Server Protocol implementation offering diagnostics, hover, completions, definition/references, rename, symbols, code actions, and semantic tokens — under Apache 2.0 at github.com/salesforce/agentscript. Because this repo is Apache 2.0, it is a viable dependency for local editor integration, CI linting, or custom validation. The execution runtime is not open source: "Agent Script compiles to a Salesforce-internal specification format that executes on Salesforce infrastructure," so "you can parse, lint, compile, and build tooling around Agent Script, but running agents requires Salesforce's runtime environment."
LLM-Driven Orchestration vs. Legacy Einstein Bot FSM
Legacy Einstein Bots used a finite state machine (FSM): dialog flows defined explicit transitions between states, and conversation routing was deterministic. Agentforce agents use LLM-driven orchestration: the GenAiPlanner uses a language model to decide at runtime which topic to invoke and which action to execute within that topic, based on the user's utterance and the planner instructions.
This distinction has direct implications for metadata authoring. In an FSM bot, gaps in dialog states produce predictable fallthrough behavior. In an Agentforce agent, poorly written topic descriptions or action descriptions cause the LLM to route incorrectly — routing issues manifest as wrong-topic invocation or no-topic fallback, not as errors in the metadata itself. Debugging a misbehaving agent therefore requires reviewing the natural-language content of topic and action descriptions, not just the structural validity of the metadata.
sf agent test run — CLI-Based Agent Testing
The sf agent test run command (Beta as of Spring '26) executes automated agent tests defined as .aiTest metadata records against a deployed agent in a target org. Tests specify input utterances and expected outcomes (expected topic classification, expected action invocation, or expected response text patterns). This command is the primary mechanism for verifying agent behavior in CI pipelines without a live UI session.
sf agent test run \
--spec force-app/main/default/aiTests/myAgentTest.aiTest-meta.xml \
--target-org MySandbox \
--wait 10
Key behaviors:
- Requires the agent to be Active in the target org. Running
sf agent test run against an Inactive or Draft agent returns an error.
- Tests run asynchronously in the org. The
--wait flag sets a polling timeout in minutes (default 5).
- Exit code 1 indicates test failure or timeout; exit code 0 indicates all assertions passed.
- As a Beta feature, the command signature and assertion schema may change between Salesforce releases. Pin the
@salesforce/plugin-agent CLI plugin version in CI pipeline tooling.
Common Patterns
Pattern 1: Source-Control-First Agent Development
When to use: Creating a new Agentforce agent or making topology changes (new topics, restructured actions) and managing them through version control with a CI/CD pipeline.
How it works:
- Initialize the agent definition with the Salesforce CLI:
sf agent generate agent --name MyServiceAgent --target-org DevSandbox
This scaffolds a .agent file and the corresponding GenAiPlugin/BotVersion stubs in the DX project.
- Edit the
.agent file in VS Code with the Agentforce extension active. The LSP provides inline validation.
- Iterate on
spec.plannerInstructions, topic descriptions, and action references locally.
- Deploy the full metadata bundle:
sf project deploy start \
--metadata Bot:MyServiceAgent \
--metadata BotVersion:MyServiceAgent.v1 \
--metadata GenAiPlannerBundle:MyServiceAgent \
--metadata GenAiPlugin:MyServiceAgent_TopicOne \
--target-org DevSandbox
- Activate the agent in the target org (activation is not scriptable via Metadata API; it is a UI action).
- Run agent tests:
sf agent test run --spec force-app/.../myTest.aiTest-meta.xml --target-org DevSandbox
Why not UI-only authoring: UI-only authoring produces configuration that lives only in the org. It cannot be reviewed in pull requests, rolled back deterministically, or promoted through a pipeline without manual re-entry. YAML-based authoring provides auditability, reviewability, and repeatable deployment.
Pattern 2: Retrieve-Before-Edit to Prevent State Divergence
When to use: Any time an agent has been modified in the Agentforce Builder UI and those changes need to be reconciled with the source-control version.
How it works:
- Retrieve the current deployed state before editing:
sf project retrieve start \
--metadata Bot:MyServiceAgent \
--metadata BotVersion:MyServiceAgent.v1 \
--metadata GenAiPlannerBundle:MyServiceAgent \
--metadata "GenAiPlugin:MyServiceAgent_*" \
--target-org DevSandbox
- Diff the retrieved files against the local working copy using Git.
- Resolve conflicts before committing. Pay special attention to
plannerInstructions blocks — these are plain-text fields and are overwritten entirely by each retrieve.
- Commit the reconciled state, then redeploy from the branch.
Why not skip the retrieve: Deploying stale .agent files over a more recent org state can silently regress changes made in the Builder UI. In production, this can take an active agent and replace its instructions with an older version.
Pattern 3: Debugging Routing Failures with DSL Content Inspection
When to use: An agent routes to the wrong topic or falls back to "I can't help with that" even when the topic clearly applies.
How it works:
- Open the
.agent file and review spec.topics[*].description for each topic. The description is the primary signal used by the LLM planner for classification.
- Check for overlapping descriptions across topics — the planner may be ambiguous about which topic to invoke.
- Review
spec.plannerInstructions for any negative constraints that might be inadvertently excluding valid queries.
- Redeploy updated descriptions, retest with
sf agent test run.
- If the problem persists, retrieve the latest
GenAiPlugin XML for the affected topic and verify the description stored in the org matches the .agent file.
Decision Guidance
| Situation | Recommended Approach | Reason |
|---|
| New agent with no source control history | Generate with sf agent generate agent, build in VS Code with LSP | Establishes source-of-truth in version control from the start |
| Agent exists only in org, needs to move to source control | Retrieve all metadata layers, commit, then manage via pipeline | Retrieve-first prevents state divergence from the first deploy |
| API v60–v63 project (older sandbox) | Use GenAiPlanner, not GenAiPlannerBundle | GenAiPlannerBundle requires API v64+ (Spring '26) |
| CI pipeline needs automated agent testing | Use sf agent test run with .aiTest metadata | Only scriptable agent test mechanism; exit codes integrate with CI |
| Topic routing is inconsistent | Edit topic descriptions in .agent file, not action definitions | The LLM planner uses topic descriptions for routing; actions are invoked after routing |
| Agent changes made in Builder UI | Retrieve before next deploy | Prevents overwriting org state with stale source |
| Behavior must run identically every time (compliance gate, entitlement check, variable math) | Author it as a -> logic instruction in Agent Script | Logic instructions run deterministically; prompt instructions delegate to the LLM |
| Behavior is conversational (greeting, summarizing, empathy) | Author it as a ` | ` prompt instruction |
| Bootstrapping an unfamiliar agent from scratch | Start in the Builder's AI Assistance view, then refine in Script view | AI Assistance turns intent into structured components; Script view is the source-control-aligned surface |
| Need local linting/validation of Agent Script in CI | Use the open-sourced salesforce/agentscript parser/linter/compiler (Apache 2.0) | Apache-2.0 license permits local tooling; the runtime stays proprietary but authoring tooling does not require it |
Recommended Workflow
Step-by-step instructions for an AI agent or practitioner working on this task:
- Confirm environment — verify the Salesforce CLI version,
@salesforce/plugin-agent plugin version, and target org API version. Confirm whether the project targets GenAiPlanner (v60–v63) or GenAiPlannerBundle (v64+).
- Retrieve current state — before making any changes, retrieve the Bot, BotVersion, GenAiPlannerBundle/GenAiPlanner, and all GenAiPlugin records from the target org. Diff against the local working copy and resolve conflicts.
- Author or edit the
.agent file — use VS Code with the Salesforce Agentforce extension. Address any LSP diagnostic warnings before proceeding. Pay particular attention to topic description quality and the plannerInstructions block.
- Deploy the full metadata bundle — deploy Bot, BotVersion, GenAiPlannerBundle, and all GenAiPlugin records together. Never deploy a subset of the bundle.
- Activate the agent — navigate to Setup > Agentforce Agents (or Agentforce Builder) and explicitly activate the agent in the target org. Activation is not automated via Metadata API.
- Run agent tests — execute
sf agent test run against the target org. Review output for routing failures, action invocation failures, or assertion mismatches. Iterate on topic or planner instruction content as needed.
- Commit and promote — commit the full metadata bundle (
.agent file plus all generated XML) and promote through the pipeline. Repeat activation in each target org after deployment.
Review Checklist
Run through these before marking work in this area complete:
Salesforce-Specific Gotchas
Non-obvious platform behaviors that cause real production problems:
- GenAiPlannerBundle requires API v64+ — projects with
apiVersion set to v63 or lower in sfdx-project.json cannot use GenAiPlannerBundle. Attempting to deploy it produces a confusing "Unknown type" error. Set apiVersion: 64.0 or higher before building Spring '26 agents.
- Activation is not deployable — the Active/Inactive/Draft state of an agent is not part of any deployable metadata record. Every org promotion requires a manual activation step in Setup. Pipelines that skip post-deploy activation steps will leave agents in Inactive state silently.
- Partial bundle deploys cause state divergence — deploying GenAiPlugin records without the BotVersion, or vice versa, can leave the org in a state where the deployed agent references metadata that does not match the deployed plugin set. Always deploy the full bundle atomically.
- Topic description content drives routing, not metadata structure — structural validity (correct YAML schema, correct XML element nesting) does not guarantee correct agent behavior. LLM routing is based on the natural-language quality of topic descriptions. A valid deploy of a well-structured
.agent file with vague topic descriptions produces a broken agent with no metadata errors.
sf agent test run requires an Active agent — running agent tests against a Draft or Inactive agent returns an error that is easy to misread as a CLI or credential problem. Always confirm the agent is Active in the target org before running CI test jobs.- Agent Script is whitespace-sensitive — like Python or YAML, indentation carries structure. A misindented
-> logic block or | prompt block silently changes which subagent or reasoning block an instruction belongs to. Configure the editor to show whitespace and never mix tabs and spaces in a .agent/script file.
- Deterministic vs. LLM behavior is a choice you make, not a default — putting a business rule in a
| prompt instruction instead of a -> logic instruction hands a guarantee-required check (entitlement, compliance gate, variable math) to the LLM, which may not honor it every time. Behavior that must be reliable belongs in -> logic instructions; only conversational nuance belongs in | prompt instructions.
- "Topic" and "subagent" are the same thing after April 2026 — the rename is terminology only, with no functional change. Older
.agent/GenAiPlugin metadata still uses topic-per-GenAiPlugin structure, while newer Agent Script authored in the Builder uses blocks. Do not treat a diff that only swaps this vocabulary as a behavioral change.
Output Artifacts
| Artifact | Description |
|---|
.agent YAML file | Declarative agent definition for VS Code authoring and version control |
| Agent Script source | Hybrid-reasoning script (-> logic and ` |
| GenAiPlugin XML records | Metadata files for each topic, containing topic description and action references |
| GenAiPlannerBundle XML | Metadata linking the planner instructions and plugins to the BotVersion |
| BotVersion XML | Agent container with channel settings and GenAiPlannerBundle reference |
| sf agent test run commands | CLI invocations for CI pipeline agent testing with exit-code integration |
| Metadata deploy command set | Full sf project deploy start commands targeting the complete agent bundle |
Related Skills
agentforce/agentforce-agent-creation — use for UI-driven agent setup, channel assignment, activation, and lifecycle management in Setup.agentforce/custom-agent-actions-apex — use when the problem is implementing invocable Apex methods that serve as agent actions, not the metadata layer.agentforce/agent-channel-deployment — use when the problem is configuring Embedded Service, Messaging for Web, or Agent API channel surfaces.devops/scratch-org-management — use when agent development involves scratch orgs, unlocked packages, or org shape-based environment management.
