A Practical Guide to Building AI Agents — Does Your Business Need One?

Companies are slowly starting to implement AI Agents into their workflow

This years SITS conference had a heavy AI aspect to it. It's becoming clear now that AI agents are no longer an experimental technology confined to tech labs. In 2025, they are a trending topic across boardrooms and business conferences — not just among early adopters in tech, but in sectors as diverse as finance, healthcare, retail, and manufacturing. They are actively reshaping how businesses operate, automating complex workflows, and enabling lean teams to do more with less. But the real question is: Does your business actually need one?

The answer increasingly points to yes — if you value operational efficiency, scalability, and competitive edge.

This guide offers a clear, business-focused roadmap to AI agent adoption. We define what AI agents are, show how they’re already adding value, and walk through how you can start building your own.

What Is an AI Agent?

An AI agent is an autonomous software entity capable of perceiving its environment, making decisions, and acting to achieve specific goals. Modern AI agents use tools like GPT-4, connect to APIs, integrate with company data, and perform tasks based on context and objectives — without constant human input.

They can:

• Read and write emails

• Schedule meetings

• Update CRMs and databases

• Analyze documents and generate reports

• Perform multi-step workflows

  
  

Think of them as junior digital employees with a growing capacity to learn, act, and collaborate.

Real-World Examples of AI Agents in 2025

Healthcare: Mayo Clinic

Mayo Clinic uses AI agents to support diagnostic processes. Their system helps analyze patient data to flag early signs of cancer, leading to a 30% increase in early detection rates.

Automotive: Tesla

Tesla employs hierarchical AI agents in its self-driving stack. These agents manage navigation, obstacle detection, and control in real-time, contributing to a 50% reduction in accidents per mile compared to human drivers.

Retail: Walmart

Walmart is testing AI shopping agents that help customers by searching, comparing, and recommending products across multiple channels. These tools also assist logistics teams with dynamic inventory management and delivery planning.

Banking: JPMorgan Chase

In 2025, JPMorgan's new internal AI assistant helps analysts prepare pitch books by summarizing financial reports, fetching market data, and generating presentation-ready content, cutting time spent by over 40%.

SMBs: Apex Realty (Fictional Case Study)

A mid-sized real estate firm, Apex Realty, deployed an internal AI agent integrated with their CRM, Slack, and scheduling tools. The agent:

• Auto-scheduled property viewings

• Responded to new leads within minutes

• Updated listings across multiple real estate platforms

• Sent weekly performance summaries to management

Result: Agents saved 5+ hours weekly, response times improved, and operations scaled with no additional hires.

A Step-by-Step Guide to Building AI Agents for Business

1. Define the Objective

Start with a clear, narrow goal. What task drains time but follows a predictable pattern?

• Drafting emails?

• Compiling weekly reports?

• Updating internal systems?

2. Map the User Interaction

Define inputs (emails, data, user prompts) and expected outputs. Example: When a lead fills out a form, the agent should draft a personalized response and schedule a call.

3. Select the Right Technologies

• Language Model: GPT-4, Claude, Gemini, or similar

• Execution Framework: LangChain, CrewAI, AutoGen

• Tools: Zapier, Make, APIs for email/CRM/calendar

4. Design the Agent Workflow

Outline how the agent will:

1. Observe (trigger event or prompt)

2. Decide (process input using LLM)

3. Act (perform actions via tools or APIs)

4. Learn (store context, adjust over time)

5. Integrate with Business Systems

Use APIs and connectors to sync with your tech stack: CRM, HR tools, databases, calendars, email platforms.

6. Test in a Sandbox

Run the agent in a controlled environment. Measure:

• Task accuracy

• Time saved

• User satisfaction

7. Deploy and Monitor

Once validated, deploy to a small team. Monitor usage and feedback. Improve iteratively.

Of course, there are thousands of tech companies that can help you with this but the steps outlined about give you a sense of what the process of creating an AI for your company would entail. Getting ahead of the game with the help of an expert will put you ahead of the growing competition in this domain.

Why Now?

Six factors make 2025 the perfect time to act:

• AI agents are mainstream: According to Gartner, by 2026, over 70% of enterprises will have integrated AI agents into at least one core business function, up from just 15% in 2023.

• The tech stack is ready: Tools like LangChain, CrewAI, and advanced language models are maturing rapidly, lowering the technical barrier to adoption.

• Pressure to innovate is high: A 2025 McKinsey survey reports that 58% of executives cite intelligent automation as a top-three priority for digital transformation this year.

• AI agents are mainstream: No longer hype, they’re being built and deployed across industries

• The tech stack is ready: Advanced models, better frameworks, and scalable APIs make it easier than ever

• Pressure to innovate is high: Businesses must find new ways to scale without adding headcount

  
  

Data Privacy and Security Concerns

We should also address the elephant in the room. As businesses adopt AI agents, it's essential to address data privacy and compliance. AI agents often require access to sensitive internal systems, including emails, customer data, and financial records. Companies must ensure:

• End-to-end encryption of data during transmission and storage

• Strict access controls and user permissions

• Transparent data usage policies, especially for customer-facing agents

• Compliance with regulations like GDPR, HIPAA, and CCPA where applicable

Organizations should also consider using private, on-premise deployments of AI models or work with vendors that offer enterprise-grade security protocols. AI is already under a lot of scrutiny regarding the its dubious use of data as it scrapes through the web to feed its LLM. Big tech AI firms are already facing lawsuits and general public outrage by creatives who accuse it of using their data to train their technology. A notable example of this was Microsoft's LinkedIn Premium customers who alleged the social media platform disclosed private messages to third parties without permission in order to train generative AI models.

Final Thoughts

Ask yourself: Where is your team wasting time today? If the answer involves repetitive digital tasks, fragmented systems, or long response times, then AI agents could be the answer.

Building AI agents isn't just for startups or tech giants. It’s a realistic, strategic move for any company looking to automate repetitive tasks, increase responsiveness, and unlock new efficiencies. Start with one workflow. Build an agent to handle it. Then scale.

Because the future of work won’t be defined by those who work harder, but by those who work smarter — with agents at their side.