Master the art of prompting by learning the structure, elements, and best practices to craft prompts for LLMs, including context, participants, grounding, constraints, and tone.

Explore zero-shot, few-shot, and chain-of-thought prompting techniques to enhance AI reasoning and decision making. Apply these methods to streamline sales workflows in a Salesforce context.

Explore prompt templates as reusable, scalable prompts with placeholders, and learn to build and test them in Salesforce using the prompt builder, data sources, and flows for b2c marketing communications.

Explore how the agent force and data cloud, via data 360, unify structured and unstructured data to create a data-aware, context-aware AI agent with secure, governance-driven architecture.

Create and configure a Salesforce developer org to enable agent force, including signing up, resetting passwords, enabling data cloud and Einstein, and choosing between legacy agent builder or agent script.

Dive into the sub-agent building block, its name, instructions, reasoning, and actions, and learn how the agent router routes to the right sub-agent via llm-based or deterministic routing.

Explore how sub-agents drive conversations via the start agent and agent router, guided by the reasoning engine, deterministic logic, and hybrid reasoning to route, execute actions, and ground responses.

Define actions with unique names, clear descriptions, precise inputs and outputs, and targets, while applying best practices like require user confirmation, progress indicators, and smart input binding for structured results.

Activate the sub-agent, create and assign a permission set to the employee, then attach it to the agent and enable end-user access with testing via the agent force builder.

Create and activate a Salesforce flow that updates a case owner by fetching the user by username, validating the ID, and handling success and fault paths.

Expose your custom actions in the legacy agent force builder by creating the agent force asset for each action, then configure inputs, outputs, and loading text with flows.

Expose Apex logic to the agent and flows via the invocable method. Use wrapper classes and collection-based inputs/outputs with bulkification and strong security practices.

Learn to build a custom Apex action that calculates an escalation score from multiple parameters using an invocable method, with case ID input and high risk classification.

Learn to create and test an Apex invocable action for calculating case escalation scores within the legacy agent force builder.

Learn to generate a personalized welcome email with the sales email prompt template by grounding recipient, sender, and organization data in a Trailhead playground using Einstein for sales.

Create a record summary prompt template to generate a grounded, rich Salesforce case summary using the record snapshot, case data, and related lists.

Ground account data with apex grounding to compute an account health score inside a flex prompt template, using revenue, case status, and stage name for a relationship manager's customer review.

Explore the execution flow of an agent script, detailing five steps from starting agent routing to after reasoning, including deterministic logic, prompt buffer, llm reasoning, and optional tool calls.

Build and test balance transfer automation using flows. Fetch and validate accounts, update balances, log transactions, and return transaction IDs for agent script invocation.

Invoke Salesforce agents via the agent api using rest: authenticate with a connected app, start sessions, and exchange messages with synchronous or streaming endpoints.

Learn to deploy and invoke Salesforce agents from Slack by configuring the Slack platform connector, approving connections, mapping users, and exposing a legacy agent as a Slack app.

Create an invocable apex action to fetch cases by contact email. Return success, message, case count, and a formatted case summary, with permission set and sharing checks.

Explore how to build custom agent UIs with lightning type overwrite in Salesforce, replacing generic UI with branded, structured inputs and rich outputs.

Define a lightning web component that captures user inputs via the lightning agent force input, using handleInputChange and the value change event to update state for the planner.

Fix lead creation errors by adding no-argument constructors for the lead request and lead DTO, and switch the booklet component to custom lightning input and output types.