Salesforce’s Agentforce Agent API is a powerful tool designed to interact with Einstein-powered AI Agents. It allows applications to send queries, receive responses, and automate conversations through Salesforce's intelligent agents. In this guide, we'll walk beginners through the essentials, including authorizing connections, managing conversation modes, building a practical example, and exploring real-world use cases.

1. What is Agentforce Agent API?

The Agentforce Agent API enables seamless integration with Einstein-powered AI Agents in Salesforce. It provides mechanisms for automated conversations, allowing developers to build applications that ask questions, process responses, and automate intelligent workflows.

In simple terms, it is like having an automated Salesforce assistant available to answer your queries programmatically.

Agentforce powers intelligent, autonomous systems that:

• Automate routine tasks

• Enhance personalization

• Scale operations efficiently

Agents span multiple business functions:

• Sales: Lead Management, Engagement, and Churn Prevention Agents

• Service: Proactive Outreach, Order and Refund Processing, Triage and Routing

• Marketing: Sentiment Analysis, Content Generation, Customer Journey Optimization

• E-commerce: Product Recommendation, Dynamic Pricing, Inventory Management

Agentforce platform capabilities include:

• Agent Topics: Job-to-be-done definitions

• Agent Instructions: Rules and guidance for agents

• Agent Skills: Built-in, custom, and partner-provided capabilities

• Agent Permissions: Strict access governance

• Guardrails: Security and safety controls

• Knowledge Integration: Access to structured and unstructured information

The API offers:

• Synchronous and Streaming conversations

• Full programmatic extensibility

• Secure invocation from anywhere

Developers can:

• Trigger Agentforce programmatically

• Embed Agentforce in any custom app

• Enable agent-to-agent communications

2. Authorizing the Connection to the API

Connecting to the Agentforce API involves secure authentication using OAuth 2.0, specifically the Client Credentials flow.

Here’s how you authorize:

import requests

url = "https://your_salesforce_domain/services/oauth2/token"
payload = {
    "grant_type": "client_credentials",
    "client_id": "your_client_id",
    "client_secret": "your_client_secret"
}

response = requests.post(url, data=payload)
data = response.json()

if "access_token" not in data:
    raise Exception(f"Authorization failed: {data}")

access_token = data["access_token"]

This token is required for every API call.

3. Sync versus Async Conversations

Agentforce API supports two conversation modes:

• Sync (Synchronous): A blocking HTTP request where the application waits for a response.

  • Pro: Simple to implement

  • Con: No UI feedback while waiting

• Async (Asynchronous): Uses Server-Sent Events (SSE) where the server streams data to the client.

  • Pro: Dynamic UI feedback thanks to streamed events and chunking

  • Con: Slightly more complex implementation due to event-driven architecture

Choose Sync for simplicity, or Async for dynamic user experiences.

4. Small Practical Example with Code

Here’s a small example interacting with an AI Agent using synchronous mode.

Step 1: Create a Session

import uuid, requests

headers = {
    "Authorization": f"Bearer {access_token}",
    "Content-Type": "application/json",
}

session_url = "https://api_instance_url/einstein/ai-agent/v1/agents/your_agent_id/sessions"
body = {
    "externalSessionKey": str(uuid.uuid4()),
    "instanceConfig": {"endpoint": "your_salesforce_domain"},
    "featureSupport": "Sync",
    "bypassUser": True
}

session_response = requests.post(session_url, headers=headers, json=body)
session_data = session_response.json()

if "sessionId" not in session_data:
    raise Exception(f"Session creation failed: {session_data}")

session_id = session_data["sessionId"]

Step 2: Ask a Question

message_url = f"https://api_instance_url/einstein/ai-agent/v1/sessions/{session_id}/messages?sync=true"
message_payload = {
    "message": {
        "sequenceId": 1,
        "type": "Text",
        "text": "Tell me the count of orders for email ID 123@gmail.com"
    },
    "variables": []
}

message_response = requests.post(message_url, headers=headers, json=message_payload)
message_data = message_response.json()

if "messages" not in message_data or not message_data["messages"]:
    raise Exception(f"Message sending failed: {message_data}")

answer = message_data["messages"][0]["message"]["text"]

print("Agentforce Response:", answer)

Step 3: Close the Session

close_url = f"https://api_instance_url/einstein/ai-agent/v1/sessions/{session_id}"
requests.delete(close_url, headers=headers)

5. Real-World Agentforce Use Cases

Sales Agents

• Auto-categorize leads

• Track customer engagement

• Predict and prevent customer churn

Service Agents

• Send proactive notifications

• Handle refunds automatically

• Classify and route queries

Marketing Agents

• Analyze customer sentiment

• Generate marketing content

• Personalize landing pages

E-commerce Agents

• Recommend products

• Adjust pricing dynamically

• Manage inventory

6. Recommended Reading

Expand your knowledge with Salesforce’s official resources:

• Salesforce Agent API Getting Started Guide

These resources will help you build powerful AI-driven applications faster and smarter.

7. Testing Your Agentforce Agents

Building an AI agent is only half the story. You need to ensure it performs reliably.

Tools like TestZeus can:

• Simulate real-world queries

• Validate accuracy, completeness, and context

• Detect edge cases before production

Testing ensures your agents are trustworthy, effective, and production-ready.

Salesforce's Agentforce Agent API opens the door to a world of AI-driven automation. With strong foundations in authentication, conversation management, and rigorous testing, you can unlock the full power of intelligent agents in your Salesforce ecosystem.