How to Create AI Agents from Scratch Using Agentforce?

With Agentforce, businesses can easily create, customize, and deploy AI-powered assistants using low-code tools like Agent Builder, Model Builder, and Prompt Builder. These AI agents can assist customers, process data, make informed decisions, and integrate seamlessly with existing business applications.  

A recent Salesforce survey revealed that 75% of small and medium-sized businesses (SMBs) are at least experimenting with AI, with growing businesses leading in adoption at 83%.

This guide walks you through the entire process of building AI agents using Agentforce—from conceptualization and customization to testing and deployment.

What Are AI Agents?

AI agents are intelligent software entities designed to observe, analyze, and act on behalf of users. They can handle tasks such as answering customer inquiries, automating repetitive work, and making predictive decisions based on data.

“Artificial intelligence and generative AI may be the most important technology of any lifetime.” 

– Marc Benioff, Chair, CEO, and Co-Founder of Salesforce

Different Levels of AI Agent Autonomy

Assistive Agents (Copilots)

Assistive AI agents function as virtual copilots, working alongside human users to enhance productivity. These agents don’t make independent decisions but suggest actions, provide recommendations, and assist with decision-making based on real-time data and user inputs.

Autonomous Agents

Autonomous AI agents can analyze situations, process complex data, and execute tasks without requiring constant supervision. However, they maintain the flexibility to escalate issues to human operators when needed.

Guardrails and the Einstein Trust Layer

Agentforce integrates guardrails and the Einstein Trust Layer to prevent AI-generated misinformation, biased responses, or data security risks. These features enforce compliance with industry regulations while maintaining AI transparency and accountability.

Source: Grand View Research

Getting Started with Agentforce

Step 1: Define Your AI Agent’s Purpose

The first step in building an AI agent is to define its purpose and scope. Understanding the specific role your AI agent will play ensures a structured and goal-driven development process.

Key considerations when defining your AI agent:

• Who is the target audience? Will the agent serve customers, employees, or business partners?
• What problem does it solve? Does the AI agent enhance customer service, automate data analysis, or improve workflow efficiency?
• Which communication channels will it use? Will it operate via web chat, email, social media, or voice-based interactions?
• What tasks should it handle? Should the agent answer FAQs, generate reports, schedule meetings, or process orders?

Once you have clarity on these aspects, you can start designing the AI agent’s functionality.

Step 2: Use Agent Builder for Easy Customization

Agent Builder is a low-code development tool within Agentforce that simplifies AI agent creation. It enables you to define topics, configure responses, and integrate actions without deep programming knowledge.

How to Use Agent Builder Effectively

1. Create a “Job to be Done” Statement
   Define the primary function of your AI agent. Example: “An AI agent that assists customers with order tracking and refund requests in an e-commerce business.”
2. Define Topics and Actions
   • Topics represent areas of focus for the AI agent. For example, a customer service agent may handle billing inquiries, order tracking, and technical support.
   • Actions are specific tasks performed by the AI agent, such as retrieving order details, processing returns, or escalating complex issues to a human representative.
3. Train the AI Agent with Natural Language Instructions
   • Use clear and structured instructions to help the AI agent interpret user requests correctly.
   • Example instruction: “If the user asks about an order status, request the order number, then retrieve order details from the database and provide an estimated delivery time.”
4. Build a Library of Predefined Actions
   • Configure predefined action flows that allow the AI agent to execute tasks efficiently.
   • Actions may include CRM lookups, appointment scheduling, or customer authentication.

Also Read – Exploring Agentic AI Career Opportunities

Step 3: Test Your AI Agent

Before deployment, rigorous testing is essential to refine the agent’s accuracy and responsiveness.

Testing Methods in Agent Builder

• Simulate User Interactions
  • Run test scenarios to observe how the AI agent responds to various user inputs.
  • Example test: “Track my order” → The AI agent should retrieve order details and display estimated delivery times.
• Refine Responses and Workflows
  • Modify decision trees, response logic, and escalation rules based on test results.
  • Example: If an agent provides incorrect tracking details, update its data retrieval logic for improved accuracy.

Step 4: Integrate Custom AI Models with Model Builder

To enhance AI decision-making, Model Builder allows developers to integrate custom AI models or pre-trained large language models (LLMs).

Steps to Integrate Custom AI Models

1. Register and Activate AI Models
   • Load custom machine learning models or connect to third-party LLMs like OpenAI’s GPT.
   • Example: Use an LLM-powered chatbot for more natural and dynamic responses.
2. Test AI Model Responses
   • Use Model Builder’s playground to test how AI-generated responses align with business needs.
   • Ensure AI outputs are accurate, relevant, and free from biases.
3. Deploy AI Models for Real-Time Use
   • Once optimized, activate the AI model within your Agentforce AI agent.
   • This enables better reasoning, contextual awareness, and decision-making capabilities.

Step 5: Optimize AI Prompts for Natural Conversations

Prompt Builder refines AI-generated responses by customizing the way AI interacts with users.

How to Use Prompt Builder Effectively

1. Customize Default Prompts
   • Modify prebuilt prompt templates to align with brand tone and user expectations.
   • Example: A banking chatbot should use a formal and professional tone, while a gaming assistant can adopt a casual and engaging style.
2. Embed Prompts into AI Workflows
   • Integrate tailored AI prompts into action flows for seamless AI-to-human transitions.
   • Example: If an AI agent detects frustration in user tone, it can escalate the issue to a human support agent.
3. Test and Optimize Prompt Effectiveness
   • Conduct A/B testing to determine which prompt structures yield the most engaging user interactions.
   • Example: Test variations of welcome messages to optimize user engagement rates.

Step 6: Deploy Your AI Agent Across Multiple Channels

Once your AI agent has been built, tested, and refined, it’s time for deployment. Agentforce allows businesses to deploy AI agents across websites, mobile applications, CRM systems, and messaging platforms like WhatsApp, Slack, and Microsoft Teams.

Steps to Deploy an AI Agent

1. Select Deployment Channels
   • Determine where your AI agent will interact with users.
   • Example: A customer support agent may be deployed on a website chatbot and WhatsApp, while an internal HR agent could be embedded in Slack or Microsoft Teams.
2. Activate the AI Agent
   • Use Agent Builder’s deployment feature to publish the agent.
   • Ensure API connections are established for seamless integration with Salesforce, Data Cloud, or third-party software.
3. Monitor Performance in Real-Time
   • Utilize Agentforce’s analytics dashboard to track response accuracy, resolution time, and user engagement.
   • Example: If customers drop off mid-conversation, refine AI workflows for smoother interactions.

Step 7: Track AI Agent Performance Metrics

After deployment, continuous monitoring is essential to maintain AI accuracy and efficiency. Agentforce provides built-in analytics and observability tools to measure AI agent effectiveness.

Key Metrics to Track

• User Interaction Rates – Are users actively engaging with the AI agent?
• Resolution Accuracy – How often does the AI agent provide correct solutions?
• Escalation Frequency – How frequently does the AI agent escalate issues to human operators?

Also Read – What Is Agentic AI, and How Will It Change Work?

Refining AI Responses Over Time

1. Analyze Conversations for Improvements
   • Identify recurring user concerns and misinterpreted queries.
   • Example: If users repeatedly ask, “Can I reschedule my order?” but the AI agent doesn’t recognize the request, refine its topic classification.
2. Optimize AI Model Training
   • Regularly update AI training datasets with new user interactions.
   • Fine-tune LLMs (large language models) to improve contextual accuracy.
3. Enhance User Experience with Feedback Loops
   • Allow users to rate AI agent responses for continuous learning.
   • Example: Implement “Was this helpful?” prompts at the end of each AI-driven conversation.

Step 8: Utilize Advanced AI Functionalities

Agentforce provides a suite of AI-powered enhancements that allow businesses to scale AI agent capabilities while maintaining compliance and security.

Einstein Trust Layer for Data Security

• Ensures secure AI interactions with built-in privacy measures.
• Features data masking, compliance verification, and toxicity detection to prevent AI misuse.

Shared Metadata for AI Governance

• Establishes universal business rules for data access, permissions, and workflow automation.
• Example: Salesforce metadata framework ensures AI agents respect CRM data security policies.

AI Test Center for Quality Assurance

• Standardized batch testing framework for AI agent performance evaluation.
• Validates prompt templates, retrieval-augmented generation (RAG), and response accuracy before live deployment.

“Generative AI has the potential to change the world in ways that we can’t even imagine. It has the power to create new ideas, products, and services that will make our lives easier, more productive, and more creative. It also has the potential to solve some of the world’s biggest problems, such as climate change, poverty, and disease”

• Bill Gates, Co-Founder of Microsoft

Use Cases of AI Agents Built with Agentforce

1. AI-Powered Customer Support Agents

Businesses can deploy AI agents to handle high volumes of customer queries, reducing dependency on human agents while maintaining 24/7 availability.

• Example: An e-commerce chatbot assists users with order tracking, refund processing, and product recommendations.
• Benefit: Improves customer response times and enhances satisfaction.

2. Sales Automation Agents

Sales teams can use AI agents to automate lead qualification, follow-ups, and personalized outreach.

• Example: An AI sales assistant identifies high-priority leads, sends follow-up emails, and schedules meetings with potential clients.
• Benefit: Streamlines the sales pipeline, allowing reps to focus on closing deals.

3. AI Agents for HR & Employee Management

Organizations can integrate AI agents into internal HR systems to assist employees with routine inquiries.

• Example: An HR chatbot provides information on leave policies, payroll, and employee benefits.
• Benefit: Reduces HR workload and provides instant assistance to employees.

4. AI-Driven Data Analytics Assistants

AI agents can analyze vast amounts of business data and generate insights in real time.

• Example: A financial AI agent identifies spending trends and forecasts revenue for business decision-making.
• Benefit: Enhances data-driven decision-making and operational efficiency.

Also Read – How to Become an Agentic AI Expert in 2025?

Conclusion

Building AI agents from scratch no longer requires extensive coding expertise. Agentforce empowers businesses to effortlessly design, tailor, and implement AI-driven assistants using intuitive low-code tools such as Agent Builder, Model Builder, and Prompt Builder.

From defining the agent’s purpose to refining AI interactions and deploying across multiple platforms, Agentforce empowers organizations to leverage AI automation effectively.

FAQs

1. What makes Agentforce different from other AI development platforms?

Agentforce is designed specifically for Salesforce users, offering seamless CRM integration, AI governance, and enterprise-grade security.

2. Do I need coding skills to build AI agents in Agentforce?

No. Agentforce provides low-code and no-code tools like Agent Builder and Prompt Builder, making AI agent development accessible to non-technical users.

3. How does Agentforce ensure AI agents follow compliance guidelines?

Agentforce uses the Einstein Trust Layer, which includes data masking, secure gateways, and regulatory compliance mechanisms to prevent unauthorized data access.

4. Can AI agents in Agentforce integrate with external applications?

Yes. Agentforce supports third-party API integrations, allowing AI agents to interact with external databases, analytics tools, and messaging platforms.