AI agents are quickly becoming integral to business operations.
According to the 2025 State of AI in operations report from Glide, nearly half of all businesses that use AI have already deployed AI agents, and 78% of these businesses are running between 2 and 10.
What starts as a solution for a single challenge—like reducing customer support wait times—expands across departments, with companies planning for AI agents to autonomously or semi-autonomously handle even more tasks in the years ahead. Unlike general AI tools and automations, custom AI agents are built specifically for your business—using your data, following your processes, and connecting with your existing systems to produce results that one-size-fits-all AI tools can’t match.
Building an AI agent requires effort, but the payoff is real—nearly half the businesses surveyed reported the results as “transformational.”
Here’s how to create one that’s customized for your business needs.
Learn more about AI agents
Read the guide1. Set specific goals
The first step to building an AI agent is knowing what specific problem you want it to solve and what results you want it to achieve. The best use cases are routine, time-consuming tasks that require decision making but not human creativity.
For example, your customer service staff might spend too much time answering the same questions over live chat or email. An AI agent could analyze incoming requests and determine whether to escalate the issue, then assign it to the right team or respond automatically to routine inquiries.
The more specific your goal, the more impact your AI agent will have. Instead of saying “I want an AI agent that helps with customer service,” define exactly what tasks it should handle: “I need an AI agent that categorizes support tickets into priority levels, routes urgent tickets to a support team member, and reduces customer wait times by 50%.”
2. Select an AI model
An AI model is a system trained on large datasets to recognize patterns, generate outputs, and make predictions based on the data it has learned. It gives AI agents the ability to interpret information, decide on the best course of action, and carry out tasks to achieve your goals.
You may already be familiar with AI models like OpenAI’s GPT-4, Anthropic’s Claude 3.7, and Google’s Gemini 2.0 Pro. There are also open-source AI models—meaning they’re freely available for anyone to use and modify—like Ai2’s OLMo, Meta’s Llama, and Stability AI’s StableStudio.
The AI model you choose directly affects how well your AI agent performs. A model with natural language processing capabilities will generate fast, conversational responses, while one built for information retrieval is better suited for analyzing data and processing reports. Picking the wrong model can mean slow performance, irrelevant outputs, or inaccurate decisions.
Choosing the right model for your AI agent depends on what you need your agent to do and comes down to these factors.
Match the AI model’s capabilities to your task. A model that can draft automated marketing emails or customer service responses won’t be useful for processing images.
Balance the AI model’s speed and accuracy. Faster models often sacrifice some precision, while more accurate models may take longer to process requests. The right balance depends on your use case—a real-time customer support agent may prioritize speed, but for a financial analysis agent, accuracy matters more than response time.
Look at how well the AI model integrates with your existing tools. Some models connect natively, while others require APIs.
Think about cost—whether it charges per token, per use, or by subscription—and how that fits your anticipated usage and budget.
Many platforms have interactive spaces—such as Hugging Face Spaces, Claude Workbench, Perplexity Labs, and OpenAI Playground—where you can test AI models with your own data.
If selecting a model sounds complicated, platforms like Glide offer managed AI, automatically picking the best model for each task. This keeps AI up to date as models are improved or new ones are launched and removes the technical complexity, letting you focus on results instead of infrastructure.
A data scientist, AI consultant, or machine learning expert can help guide which AI model is best suited for your agent.
3. Design a user interface
An AI agent needs an interface, such as a web or mobile app, where employees or customers can interact with it.
Employees might access AI agents through web or mobile apps (or both) when completing their work. Your finance team may use an invoice processing AI agent through a web app to review and verify invoices or snap photos of invoices for processing through a mobile app.
Customers may interact with agents in similar ways—checking order history or re-ordering supplies through a web or mobile app, or getting answers to common questions through a chatbot embedded on your website.
A well-designed user interface (UI) is:
Functional—Lets users complete tasks quickly, such as generating client reports with a single click
Accessible—Works for everyone, including people with disabilities, through elements like descriptive labels for form fields and high contrast between text and background colors
User-friendly—Matches how people naturally work, with consistent layouts and clear error messages
The UI directly affects how easily people can access your AI agent’s capabilities—ideally with little to no training—and how readily they’ll adopt it into their workflows.
“A well-designed interface makes AI agents invisible to your users,” says Evan Furniss, Solutions Expert at Glide. “Data is prepared, decisions are taken, and tasks are completed all behind the scenes. The work gets done without users having to think about the technology or the process.”
The interface must also be mobile adaptive so users can access it from phones and tablets. This flexibility means your AI agent works anywhere that work happens—on sales floors, in warehouses, at client sites—rather than limiting its use to employees at desks.
Designing the UI involves product designers, who focus on making the AI agent easy to use and functional.
4. Connect your data and software
Connecting the AI agent to your data sources—like a customer relationship management (CRM) platform, inventory databases, and financial systems—gives it access to information specific to your business. This connection allows it to make decisions and take actions based on your business data rather than make assumptions.
This connection must go both ways—the agent should be able to read information and also update records. A field sales AI agent that can only read customer data in HubSpot or Salesforce might give your sales rep information, but it can’t update contact information or add meeting notes on the go. This can lead to inaccurate and incomplete data, plus the time wasted in trying to fix these inaccuracies later.
If you’re using multiple AI agents within your business, they should also connect to and share information with each other. Your contract management AI agent can add a new vendor to your invoice processing agent when a contract is signed. This coordination reduces manual work for your team and keeps your data accurate everywhere it lives.
Without proper integration to your data sources, your AI agent becomes less useful, since it won’t have access to complete, accurate data to make informed decisions.
Connecting your AI agent to your data sources typically involves software developers who understand your existing technology infrastructure and can easily connect your systems and the AI agent.
5. Deploy and monitor
After building your AI agent, put it to work and track its performance to see if it's actually achieving the objectives you set. If an AI agent is handling inspections, is it accurately identifying issues and creating proper reports?
You can use a three-stage process to test whether the AI agent is delivering the results you need.
AI generates results, but you're still doing the task—Your inspection AI agent analyzes photos and flags potential defects, but your team still conducts full manual inspections. This lets you compare the AI’s findings with your inspectors’ expertise to evaluate its accuracy.
AI operates alongside your team—Your inspection agent processes images, identifies issues, and creates draft reports based on your standards. Inspectors review these AI-generated reports, making corrections where needed and focusing their expertise on complex assessments while the AI handles documentation.
AI handles the task, while your team monitors results—If you’re designing your AI agent to be autonomous, it approves standard cases and only flags items needing human attention. Instead of manually reviewing every inspection, your team monitors reports and intervenes only when necessary.
Keep refining the agent until it consistently meets your predefined goals. Each adjustment brings it closer to delivering the specific outcomes your business needs.
Choose a method to build your AI agent
Building an AI agent starts with a decision: do you build one from scratch, customize a pre-built option, or buy an off-the-shelf solution? Each approach offers different levels of control, resource requirements, and development timelines.
The right choice depends on your business needs, technical capabilities, and available resources.
Custom-coded agent
A custom-coded AI agent needs software engineers to build its logic, workflows, integrations, and user interface from the ground up. This work involves writing code (often in Python) to establish how the agent processes information, responds to inputs, and interacts with users. Engineers must also connect the agent to business systems so it can access data, exchange information, and take action when needed.
Building AI agents from scratch is a significant investment. It takes months to develop, requires access to specialists, and comes with ongoing costs for maintenance, infrastructure, and model retraining. Businesses that have in-house technical expertise can build a custom agent themselves or work with external agencies—an option that reduces the internal workload but adds cost.
This approach makes the most sense for businesses that need full control over their AI agent, especially in highly regulated industries or when working with sensitive or proprietary data. It offers the highest level of customization but requires access to deep technical resources and a long-term commitment.
Buying off-the-shelf AI agents
Off-the-shelf AI agents offer the fastest way to get up and running. Platforms like HubSpot Breeze and SAP Joule provide pre-built AI solutions that work out of the box, often requiring only a few hours or a couple of days to deploy. But with speed comes trade-offs—these agents are built for common use cases, not industry-specific challenges or specialized business needs.
According to Glide’s research, industries like construction, manufacturing, and retail face particularly low AI adoption rates—partly because they just don’t know which tools to use or how it can help them. Off-the-shelf AI agents aren’t an option, because they don't integrate with the legacy systems these industries still rely on.
Field operations, such as sales teams or inspection crews, face a different set of issues—many of these pre-built AI agents are designed for office-based tasks such as processing emails or scheduling meetings rather than work that happens on site. These field activities require specialized interfaces, location-aware capabilities, and custom features depending on their work, which standard solutions don't provide.
Using these agents also means committing to the vendor’s ecosystem. This could mean moving your existing data or adjusting your workflows to fit within its limitations.
Off-the-shelf AI agents work best for service businesses or teams where the main tasks involve digital information and communication and don’t need customization.
Buying pre-built, customizable AI agents
Pre-built AI agents that are customizable offer a middle ground between off-the-shelf solutions and custom development. These agents come with AI capabilities built in, like natural language processing to understand user requests or pattern recognition to automate approvals. You can adapt these AI agents for your specific needs with custom workflows and integrations to existing tools and data.
Unlike fully custom development, pre-built agents can be deployed quickly, especially when using no code or low code platforms. These platforms let non-technical teams build AI agents with little to no coding, reducing reliance on AI specialists. You can start from a template or use a WYSIWYG editor with a visual interface instead of writing code.
Glide’s data suggests that, once businesses start using no code platforms for custom agents, they tend to expand quickly—nearly a quarter of businesses that use no code have deployed 11 or more AI agents. The appeal is clear: teams can see positive results and quickly apply the same approach to other areas without waiting for IT resources.
Some providers like Glide also offer industry-specific templates that address a range of use cases. This gives you a strong foundation to build custom agents rather than starting with a blank slate.
This approach is a good fit for businesses that want customized AI solutions without the inflexibility of full-scale development. It’s especially useful if you want to implement AI agents across multiple teams without heavy technical involvement.
Depending on your needs and in-house capabilities, customization can happen in different ways. You might handle customization internally, work with consultants, or partner directly with the no code platform during purchase. Platforms like Glide help you tailor pre-built agents to your specific requirements, giving you an AI agent that addresses your exact business challenges.
If you want to try one out in real life, this resume screening AI agent can be tested on your actual data and shows all the steps for how the AI makes its judgment calls between your job description and the resumes you’ve received.
Working with consultants
Here’s the truth: building an AI agent is hard. It needs specialized knowledge of APIs, webhooks, data structures, workflows, and conditional logic—technical expertise most businesses don’t have internally. You also need expertise in AI model selection, integration capabilities, data cleaning, security protocols, and user interface design.
That’s why 55% of businesses partner with consultants to build their AI agents rather than tackling this complexity alone, according to Glide’s research.
Some consultants build AI agents using traditional code, but many now use no code platforms to reduce project timelines from months to weeks while making it simpler to modify the agent. Faster development also lowers costs, avoiding the expense of drawn-out traditional development timelines.
Most consultants specialize in one or two platforms, giving them deep knowledge of the best tools for the job. They also help with the biggest barrier to AI agent adoption for many businesses—not knowing how to use AI in your operations. Consultants help businesses of all sizes across different industries solve problems with custom AI solutions. They’ve seen what works, and they can use that knowledge to help you identify exactly where AI agents fit into your business.
Learn how a leading Glide agency is powering clients’ operations with AI
Read the interviewConsultants often handle everything from identifying use cases to building and deploying your AI agent. They make the process easier and faster, freeing up your internal development teams to focus on other priorities. Many also provide ongoing support, refining your AI agents as business needs change and AI technology advances.
Build an AI agent with Glide
Businesses find AI’s impact exceeds their expectations. Glide’s research shows that while only 28% of companies expected AI to transform their operations, the reality proved far more impressive—51% of those who implemented it report it actually transformed how they work.
This difference between what businesses expect and what they experience shows why early AI adoption creates competitive advantage. That’s where Glide can help.
Glide makes it simple to build AI agents that achieve your goals without needing technical expertise. Glide’s managed AI approach takes care of selecting the right AI model for your tasks. The platform lets you automatically create mobile-adaptive interfaces that work wherever your team does—in the field, on the road, or in the warehouse—without designing or building multiple interfaces. Glide also connects AI agents with the tools your business already relies on, like HubSpot and Salesforce, so they work within your existing systems rather than adding another disconnected tool.
If you want to start with the basics of how to build an app, try this course on Udemy or visit Glide University. If you're ready to begin building AI agents, The AI Certification course will get you there faster.
Whether you work with Glide or partner with a consultant, you get access to a platform and expertise to build AI agents that fit your business.
