Sales Automation Agents and How to Build One

Sales representatives (reps) and operations (ops) teams are often overwhelmed these days juggling the explosion of tools, data, and manual tasks. In fact, up to 70 percent of their working hours are spent on non-selling activities, leaving little time for the high-impact work that actually drives sales. 

But what if we could automate the manual work, close more deals, and increase sales?  

That’s the power of sales automation agents. These AI-powered assistance can reason, act, and communicate within your sales workflows. Ask them to “mark unfinished deals, contact the owners, and prioritize important ones,” and it just gets done.  

And it’s not just hype. A recent PwC survey shows that 66 percent of companies using AI agents report higher productivity. Many are seeing up to 57 percent in cost savings and big improvements in customer experiences. Sales automation agents are fundamentally shifting our approach to efficiency, accuracy, and team productivity. 

In this article, we’ll go over what AI agents for sales are and what they can do for your team. We’ll also see how you can start building one using Domo that can grow with your business demands. 

Let’s start with the basics. 

What are sales automation agents? 

Sales automation agents are AI systems built on top of your CRM and other sales data. Their job? To handle multiple tasks, like sales-related activities, normally done by people. Only faster, smarter, and at scale. 

Unlike traditional sales automation tools that follow a rigid “if-this-then-that” script and require salespeople to provide input and make decisions, AI sales agents can analyze sales and customer data, learn from it, and act on it. 

While simple automation might add a lead to a generic email sequence, an agent could take the same lead, assess their industry and job title, select the most relevant case study, and draft a personalized outreach note referencing that content and actually connect. 

AI agents for sales generally fall into two categories based on the level of autonomy and their role in the sales process: 

• Autonomous agents
  These are able to act independently. They can engage an inbound lead, qualify them with questions, schedule a meeting on a rep’s calendar, and update the CRM with new information. They can initiate workflows, personalize recommendations, and even guide a customer toward a purchase. ‍
• Assistive agents
  These agents are like intelligent assistants that augment the work of your sales team. They can help your team surface the next steps for a rep, provide contextual alerts about a deal at risk, summarize conversations, or offer real-time guidance based on best practices. They don’t replace people but help them handle repetitive and time-consuming tasks more efficiently. 

Up next, we’ll look at use cases that show how these agents help transform your sales workflows. 

What can sales automation agents do? 

Since AI sales tools plug into your data and business applications, there are so many different real-life use cases they can take on. The most popular being to automate tasks and streamline access to customer data.  

Lead scoring and personalized outreach 

Agents with external data and CRM access can analyze the quality of every lead that comes in using predictive models to score and rank them. This helps your reps focus their efforts on the opportunities most likely to convert.  

The agent can even send highly personalized follow-up messages. They can automate conditional follow-ups that dynamically adjust based on a prospect’s behavior, such as opening an email or clicking a link. 

For instance, here at Domo, our team built a prospecting assistant that takes a company name and a prompt, like “What could I talk about with this persona?” Then it instantly pulls in data from investor reports, press coverage, and internal use case catalogs. It even suggests talk tracks, turning a 90-minute research scramble into a one-minute task. 

CRM updates  

One of the best ways to improve sales ops automation is by automating CRM updates. An agent can automatically log their sales activities like calls and emails after each interaction with a potential customer. They can then prepare a summary with the key takeaway from calls and emails, and then update the record, keeping sales data always clean, accurate, and current. 

Customer engagement 

AI agents can recognize customers’ past purchases, shopping habits, and online behavior to recommend relevant products. Moreover, they can engage with customers in a personalized manner, respond to their questions, and guide them through the buying process. 

Sales forecasting 

AI sales agents analyze historical data and current trends to predict potential future sales opportunities and provide insight into the risks. They also recommend actions to overcome the risks and improve performance. This way, you can set realistic sales goals and make informed strategic decisions. 

Always-on responsiveness 

Agents work around the clock, making sure no inbound lead or critical customer opportunity is missed outside normal business hours. This quick response can greatly improve speed-to-contact and increase conversion rates. 

You might be asking yourself, “Don't we already have automation?” To better understand why agents stand out, let’s compare them to traditional automation approaches you may already be familiar with. 

How do these agents differ from traditional automation? 

While both traditional automation and AI agents aim to make things more efficient, traditional rule-based tools struggle when things change. Sales automation with AI brings reasoning and context into the picture. Here’s a quick look at key differences: 

Ultimately, AI agents do more than just automate single tasks. They execute entire multi-step sales workflows, complete with reasoning and transparency. So, why does this matter for your team? It comes down to the three things we’ll look at next. 

What makes sales automation agents effective: Time, trust, and efficiency 

The value of introducing sales automation agents comes down to three key benefits: saving time, building trust, and driving efficiency. 

Saving time through automation 

Sales teams can save more time by automating routine administrative tasks like sending messages and entering CRM data. Sales automation agents free up teams to do more selling, building relationships, and closing deals. On average, sales teams that automate these tasks save about 6 hours per week per rep. Also, automating lead distribution can speed up response times by up to 87 percent, which helps close deals faster. 

Building trust through governance 

For sales automation to succeed, the people using it have to trust it. That trust comes from being open about how it works (transparency) and control. Agents that rely on clean CRM data and maintain built-in audit trails help teams see exactly what actions are being taken and why. You can establish usage limits, such as capping the number of actions an agent performs each day, to further build confidence. This maintains responsible use of automation while keeping human oversight in the loop. 

Improving efficiency through smart technology 

Agents add analytics-based intelligence into your workflows. They can quickly respond to potential customers, reach out to more people than a person could, and use data to target the right leads. For example, sales agents can automate up to 80 percent of outbound sales tasks, increasing reply rates from prospects by 42 percent with better messaging. Furthermore, they reduce missed deals by 39 percent and contribute to more revenue and better risk management. 

Now, let’s investigate a step-by-step guide on building a sales automation agent tailored to your business needs. 

How to build a sales automation agent 

Building a sales agent is more accessible than you might think, but it requires a thoughtful strategy and a platform that supports automation at scale, like Domo. It’s more of a strategic process that combines clear goals with clean data. Below, we outline the key steps to guide you from concept to a live agent that helps your team work smarter. 

Step 1: Define your use case and goal 

Clearly define your objectives to align the agent with current business demands and establish a way to measure its impact. Start with a high-impact sales workflow, and the good starting points include inbound lead qualification, automated meeting scheduling, or pipeline gap detection. Align your project with a measurable key performance indicator (KPI), such as reducing lead response time, increasing meeting conversion rates, or improving data accuracy in the CRM. 

Step 2: Prepare your data foundation 

Sales automation agent’s performance depends on the quality of data it uses. Before you start building it, make sure your primary data sources, like CRM records, marketing logs, deal stages, and sales activities, are accurate, organized, and easy to access.  

You can connect your data easily using our pre-built connectors, with more than 1,000 available. They let you integrate data from various platforms quickly, including Salesforce and Pardot. 

You also need to address missing values and transform data into a usable format. Having well-managed data is the most important requirement for creating a dependable and trustworthy AI agent. For that, we offer a Magic ETL tool to help you combine, clean, filter, deduplicate, and turn raw, unstructured data into information you can use.  

For a more thorough overview, our AI Readiness Guide walks through the key steps for building a solid data strategy. 

Step 3: Select your architecture and tools 

Select a large language model (LLM) or agent orchestration platform that aligns with your use case. Options include GPT‑4, custom fine-tuned models, or DomoGPT within Domo’s Agent Catalyst framework. 

Then, select the tools your agent will need to act, such as connectors for your CRM, calendar, and email APIs. For complex workflows that span multiple tasks, consider using a multi-agent or orchestration framework to coordinate actions. 

Step 4: Define agent logic and guardrails 

Here, you build the framework that brings all the components together. First, decide what inputs it will get, like a new lead form submission. Then, determine the rules it will follow, such as if the lead score is over 80, take a specific action. Next, specify what actions it will perform, like sending a certain email template. 

Provide clear instructions and safety measures, like limits on how often emails are sent and steps to follow if the situation is beyond what the agent can handle. Also, keep track of how it makes decisions, record its actions, and set up checkpoints to review its performance for governance. 

Step 5: Prototype, pilot, and iterate 

Start small, build a prototype, and test it with a team that can give honest feedback. Watch how the agent works in a controlled setting to see if it makes sense. Check if it's interpreting the data correctly and if the instructions are clear. Use this phase to get feedback, make improvements, add safety features if needed, and keep refining until it works well consistently. 

Step 6: Deploy, monitor, and govern 

Once the pilot is successful, put the agent into your live workflow. After deployment, continuously monitor its performance, error rates, and response quality. Keep a close watch on compliance and gather feedback from the sales team. Your agent will require ongoing maintenance and updates to its logic over time to remain effective. 

How Domo can simplify the journey to build AI agents for sales 

When you’re ready to build a strong and reliable AI agent for sales teams, we at Domo support you through the toughest parts of the process, including data integration, agent building, governance, and workflow orchestration. We provide an end-to-end environment that simplifies the journey from idea to a fully functional sales automation agent. 

Domo connects and governs all the sales-related data from anywhere to one place, and you can build agents you can trust. 

Our Agent Catalyst provides a low-code interface that lets you build and manage sales AI agents in just four steps. It saves time setting up agents and reduces the need for deep AI engineering or separate orchestration tools. 

Step 1: Select your LLM: Use DomoGPT (hosted securely in-platform), integrate your own model, or connect to third-party LLMs, always within Domo’s governed environment. 

Step 2: Define instructions: Specify the agent’s objectives, decision logic, escalation thresholds, and performance criteria. 

Step 3: Provide knowledge: Attach structured data sets and FileSets to enrich agent context via retrieval-augmented generation (RAG). 

Step 4: Assign tools: Enable actionable workflows, emails, alerts, CRM updates, or dashboard interactions through AI Services or custom agent logic. 

Domo prioritizes compliance in your data operations with features like data lineage, content certification, and role-based access controls. These features give your sales ops teams certainty that your agents operate safely, while maintaining CRM hygiene and adhering to regulations such as GDPR or HIPAA. 

Check out our Agent Catalyst toolkit to build, deploy, and govern AI-powered sales agents. Discover more about the agents you can create in Domo.