Building AI Agents for Business: How AI Can Transform Your Operations

AI agents are software systems that can use AI models, business data, and tools to complete tasks or workflows on a user’s behalf. Unlike a basic chatbot that mainly responds to questions, an AI agent can retrieve information, choose tools, take actions, and complete multi-step work within defined permissions.

Businesses are using AI agents to automate repetitive processes, assist employees, improve customer service, analyze information, and connect work across systems such as CRM, ERP, document repositories, and internal applications.

This article explains where AI agents can add value, the main ways businesses can build them, and what organizations should consider before moving an agent into production.

Why Are Businesses Using AI Agents?

Businesses are adopting AI agents because they can handle work that traditional automation often cannot. Instead of following only fixed rules, an agent can interpret a request, retrieve information, select an appropriate tool, and complete multiple steps within a workflow.

For example, an internal agent might answer an employee’s question by searching company documents, checking information in a CRM, and then creating a follow-up task. A customer-service agent might retrieve account information, answer a question, and route an issue to the appropriate team.

The biggest business benefits typically come from reducing repetitive work, improving access to information, increasing employee capacity, and shortening the time required to complete common processes.

Common use cases include:

• Customer and employee support
• Research and knowledge retrieval
• Document analysis
• Workflow automation
• Sales and service operations

What Is the Difference Between an AI Agent and a Chatbot?

A chatbot primarily responds to user input. An AI agent can go further by deciding what steps are needed, using tools or data sources, and taking actions within a defined workflow.

For example, a chatbot may tell an employee how to submit an expense report. An AI agent could potentially review the request, retrieve the relevant policy, validate required information, and initiate the next step in the process.

The difference is not simply the AI model being used. The agent’s value comes from the combination of the model, business context, tools, permissions, orchestration, and controls around what it is allowed to do.

What Are the Main Ways to Build an AI Agent?

Low-Code and Enterprise Agent Platforms: Platforms such as Microsoft Copilot Studio can be useful when a business wants to connect an agent to existing enterprise applications, knowledge sources, and workflows without building the entire orchestration layer from scratch. Copilot Studio now supports generative orchestration, where an agent can select tools, knowledge sources, topics, and other agents based on a user’s request.

Custom Agent Development: Businesses with more complex requirements may build custom agents using APIs, software frameworks, and agent-development tools. For example, OpenAI currently provides several development paths, including its Agents API, Agents SDK, and Responses API. These support different levels of control over tools, orchestration, state, and runtime environments.

Private or Controlled AI Environments: Organizations with specialized security, compliance, data residency, or infrastructure requirements may choose more controlled deployment approaches.

That can include private-cloud infrastructure, tightly governed access to enterprise data, or models and agent components deployed within a company’s existing security architecture.

This is a much stronger structure than presenting “ChatGPT” itself as one of the primary technical architectures.

How Does Retrieval-Augmented Generation Help an AI Agent?

Retrieval-Augmented Generation, or RAG, allows an AI system to retrieve relevant information from approved sources before generating a response. Those sources might include documents, knowledge bases, databases, or other enterprise systems.

RAG is especially useful when an agent needs current or organization-specific information that is not contained in the model’s general training data.

RAG does not automatically make an agent accurate. The quality of the results still depends on the underlying data, retrieval process, permissions, instructions, and evaluation of the agent’s responses.

What Can a Business AI Agent Do?

Depending on its permissions and integrations, an AI agent can retrieve information from systems such as SharePoint or Salesforce, analyze documents, answer questions using company knowledge, call APIs, trigger workflows, or perform defined actions in other business applications.

More advanced agents can combine several of these capabilities within one task. For example, an agent could retrieve a customer record, review supporting documents, summarize the situation, and create a follow-up action.

When Does a Business Need a More Controlled AI Environment?

Some organizations need greater control over where data is processed, how models are accessed, and which systems an agent can use. This is especially common in regulated industries or environments with strict data-governance requirements.

Depending on the use case, the architecture may involve cloud services within an existing enterprise environment, private networking, restricted data access, dedicated model endpoints, or self-hosted components.

The right approach depends on the sensitivity of the data, regulatory requirements, existing infrastructure, performance needs, and cost.

What Is the AI Agent Development Process?

Step 1: Define the Business Use Case

Start with a specific problem rather than the technology. Identify what work the agent should perform, who will use it, what outcome should improve, and what actions the agent should and should not be allowed to take.

Step 2: Identify the Data, Systems, and Tools

Determine what information the agent needs and where that information lives. This might include CRM data, internal databases, documents, APIs, ERP systems, or business applications.

Step 3: Choose the Architecture

Select the model, agent platform, integrations, retrieval approach, and hosting architecture based on the requirements of the use case.

Step 4: Build and Test the Agent

Develop the initial workflow in a controlled environment. Test not only whether the agent can complete the task, but whether it behaves reliably when information is incomplete, ambiguous, or incorrect.

Step 5: Evaluate Before Production

AI agents should be evaluated against defined scenarios before they are widely deployed. Testing should cover output quality, tool selection, failure cases, permissions, security, latency, and the points where human review is required.

Step 6: Monitor and Improve

Once deployed, monitor how the agent performs in real workflows. Review errors, unexpected behavior, user feedback, tool failures, and changes to the underlying systems or business rules.

When Should You Not Use an AI Agent?

Not every automation problem requires an AI agent. Traditional software or rules-based automation may be a better choice when a process is highly predictable, requires the same steps every time, or leaves little room for interpretation.

AI agents are most useful when a workflow requires some combination of reasoning, unstructured information, tool selection, or adaptation to changing inputs.

The goal should not be to make every process agentic. It should be to use the simplest technology that can reliably solve the business problem.

What Controls Does a Business AI Agent Need?

An AI agent should have clear boundaries around the information it can access and the actions it can perform. Those controls become increasingly important as agents move from answering questions to taking actions inside business systems.

Depending on the risk of the workflow, controls may include role-based permissions, approval steps, audit logs, restricted tools, data-access rules, human review, and limits on autonomous actions.

Higher-risk actions should generally require stronger validation and oversight than low-risk tasks such as summarizing documents or retrieving internal information.

How SOLTECH Approaches AI Agent Development

At SOLTECH, we start AI agent projects with the business workflow rather than the model. The first questions are typically: What work should the agent perform? What systems and data does it need? What decisions can it make independently, and where should a person remain involved?

From there, the architecture may include enterprise AI platforms, custom agent development, retrieval from business data, integrations with existing applications, or a combination of these approaches.

The goal is to build an agent that can perform a defined business function reliably within the organization’s technical, security, and operational requirements.

Interested in implementing AI in your business? Contact SOLTECH today to discuss your AI strategy and get started.

FAQs

What is an AI Agent, and how is it different from a chatbot?

An AI Agent is a context-aware AI system that can analyze data, automate workflows, and make intelligent decisions. Unlike traditional chatbots, AI Agents integrate deeply with business processes and enterprise systems to provide accurate, tailored responses.

How much does it cost to build an AI agent?

The cost of an AI agent depends on the complexity of the workflow, number of integrations, data requirements, security needs, model usage, testing requirements, and level of customization. A focused internal agent that retrieves information may require significantly less development than an agent that performs multi-step actions across several enterprise systems.

A discovery or proof-of-concept phase can help define the architecture and estimate the cost before a larger implementation begins.

Which AI platform is best for my business?

For most B2B companies, Microsoft Copilot Studio and Azure AI Services offer robust AI capabilities without requiring deep AI expertise. For businesses with strict compliance and security needs, Private AI Models provide a self-hosted alternative.

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Thayer Tate

Chief Technology Officer

Thayer is the Chief Technology Officer at SOLTECH, bringing over 20 years of experience in technology and consulting to his role. Throughout his career, Thayer has focused on successfully implementing and delivering projects of all sizes. He began his journey in the technology industry with renowned consulting firms like PricewaterhouseCoopers and IBM, where he gained valuable insights into handling complex challenges faced by large enterprises and developed detailed implementation methodologies.

Thayer’s expertise expanded as he obtained his Project Management Professional (PMP) certification and joined SOLTECH, an Atlanta-based technology firm specializing in custom software development, Technology Consulting and IT staffing. During his tenure at SOLTECH, Thayer honed his skills by managing the design and development of numerous projects, eventually assuming executive responsibility for leading the technical direction of SOLTECH’s software solutions.

As a thought leader and industry expert, Thayer writes articles on technology strategy and planning, software development, project implementation, and technology integration. Thayer’s aim is to empower readers with practical insights and actionable advice based on his extensive experience.