Building Your First AI Agent: Tools, Frameworks, and Best Practices

AI agents are intelligent software programs designed to perform tasks, make decisions, or solve problems autonomously or semi-autonomously. AI agents use large language models (LLMs) and other tools to manage tasks without the need for constant human intervention. They can adapt to changing environments and respond to real-time data, making them business tools that are as powerful as they are elusive, and raising the question of how to build an AI agent.

AI agents mimic human-like cognitive abilities such as learning, reasoning, problem-solving, perception, and language understanding. They are ideal for managing complex decision-making processes, and organizations all over the world are looking for ways to use them. Gartner has predicted that, by 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously. [1]

We’ll outline the tools, frameworks, and best practices for building your first AI agent, and explain why choosing the right platform and strategy is crucial for supporting the growth and scalability of AI agents.

How Organizations Can Use AI Agents

AI agents are used in diverse areas of operations, including customer service, human resources, accounting, and business analytics. They enhance productivity and user experiences by automating processes and improving decision-making. As Figure 1 shows, all departments within an organization can be improved with properly designed and deployed AI agents. Looking into even one area, like call centers, reveals numerous processes that can be augmented using AI agent development.

Figure 1: AI Agent Use Cases in Call Centers

AI agents are sophisticated, context-aware digital entities that act as operational partners in various ecosystems, driving innovation and efficiency. An individual AI agent can be constructed from modular skills and microservices, allowing its core elements to be reconfigured and adapted for other AI agents running different tasks. For example, a poll-taking skill used as part of a scheduling automation in a call center, could also be used by a marketing team voting on an array of proposed designs. Designing agents in this modular fashion is valuable even in early experiences because in agentic systems, AI agents collaborate and often work together in orchestrated swarms to manage complex interactions.

How to Choose the Right Platform and Tools

When building your first AI agent there are several key factors that will shape your development experience and the final product’s capabilities, as well as the shape of the agentic system that will emerge as you add agents and scale automation. There are different platforms and AI agent frameworks, including:

• Langchain/LangGraph, which is a framework for building with Python. There are tools for agent design, memory management, and tool integration. This is a modular approach, but the learning curve can be quite steep. 
  
  
• Vertex AI Agent Builder and OpenAI’s GPTs: Both Google and OpenAI have platforms for building and deploying AI agents. Both can be useful for building AI agents, but have issues with scalability. As a foundation for an agentic system, both platforms can become expensive to scale with customization limits that might hold organizations back.
  
  
• Platforms for building voice and conversational agents such as Voiceflow and Synthflow are also difficult to scale across an entire organization, due to narrow user cases, limited testing tools, and might pose security issues. 
  
  
• Agent platforms: In this emerging category, AI agents can be created using no- and low-code building tools in an open environment that can connect with knowledge bases, legacy software, and a vast array of tools. Platforms such as Generative Studio X (GSX) from OneReach.ai make it feasible for organizations to quickly build, test, and iterate on AI agents — deploying them into an environment where they can collaborate with other AI agents to complete high-level objectives. Open and flexible platforms such as GSX remove many of the hurdles associated with integration and scaling.

The choice of underlying large language models (LLMs) will be important from a security and trust standpoint. There are many models available in the marketplace that provide varying levels of competency across use cases at varying costs. Factors like API costs, response latency, context window size, and whether you need local deployment for security reasons should be considered, even if they aren’t among your short-term requirements. Agent platforms such as  Generative Studio X (GSX) can support multiple LLMs, giving you flexibility to experiment and switch as your needs evolve, even within the space of a single automation.

Python is the dominant programming language in the AI agent landscape, and LangChain offers well-developed libraries, machine learning ecosystems, and community support. The challenge posed by this approach is that it can be difficult for non-technical users to participate in the creation and evolution of AI agents. The fastest way for an organization to establish and scale agentic automation is by getting the people who understand the rote processes that AI agents can automate involved in building them. 

No- and low-code building tools such as GSX open agent building to those with limited technical capabilities and are often a boon to others in the organization who want to try their hands at agent building. As shown in Figure 2, it’s now possible to build an AI agent using conversational prompts. As Robb Wilson, CEO and co-founder of OneReach.ai, explains in Age of Invisible Machines — the first bestselling book about AI agents — ”The trick is to be nimble and swift … Learn to harness and ride AI agents, composable architecture, and no-code creation by creating a strategic environment where everyone in your organization can use technology effectively — and no one is left behind.” [2]

Figure 2: Building an Agent with the GSX Platform

The objective for this AI agent was generated by an LLM based on a conversational prompt asking for an AI agent to automate aspects of the billing process. Actions and flows can also be added using generative tools. The menu on the left connects the AI agent to various tools such as knowledge bases, web search, and other AI agents. There is also detailed session information tracking user interactions and the AI agents chain-of-thought. AI agents like this can become part of flows that can do all kinds of work in the real world by using tools like model context protocol (MCP) and Agent2Agent (A2A) protocol.

Know Why You’re Building an AI Agent

The most important question to answer before building an AI agent is: why?

• Why do you need an AI agent? 
• Who will use the agent?
• What process are you seeking to automate? 
• What makes an AI agent the right tool?

Some processes such as payroll processing and data entry tend to be deterministic and might not require the sophistication of an AI agent. If robot or business process automation (RPA/BPA) are already meeting requirements for individual steps, try taking a broader look at the workflow that contains them. There are usually opportunities to use AI agents to eliminate unnecessary steps and act as a glue between disparate systems.

Similarly, the LLMs at the center of an AI agent might be enough for low-level automations. If the goal is to have an agent that can guide users through frequently asked questions (FAQ), it’s fairly easy to give the AI agent access to a reliable knowledge base containing those questions and their answers. With guardrails and a few security settings in place, this AI agent could go live quickly.

The real “why” is in the processes lurking behind the questions. If the question is “How do I check the status of my order?” The answer can be automation. Rather than replying with a list of instructions on how to log in, navigate to your orders, and follow the link to the shipping company’s app, an AI agent can answer the question with an action: “Let me verify your identity and I’ll look up your recent orders.” Behind the scenes, it can connect information from a customer’s profile and use API calls to get an estimated delivery date for their most recent order, and follow up with a specific delivery timeframe.

Empower Your AI Agent with Agency

As shown in Figure 3, AI agents with real agency are connected to numerous tools and technologies. Knowing what you want to automate and what kinds of technological considerations are involved is a critical first step.

Figure 3: The Core Capabilities of an AI agent

There are a vast array of technical considerations to make when designing AI agents that will have lasting impact:

• Memory for context retention requires feedback loops. Using outputs as inputs for future action is fundamental to learning and memory.
• Tool integrations need to connect agents to databases, CRM systems, and external APIs.
• The ability for agents to show chain-of-thought reasoning allows for better guardrail calibration.
• Reflection and the ability to break tasks down into manageable chunks are pivotal to the self-improvement of agentic systems.
• Security and governance controls are critical for data privacy, access control, and auditability, especially for open-source or cloud-based solutions.

Multi-agent collaboration is the long-term goal, so think about AI agents as belonging to an ecosystem of automation. Orchestrating teams of agents to collaborate on complex tasks should be the goal even as orgs are considering individual test deployments. Above all else, orgs should be looking for opportunities to design AI agents that can improve on existing workflows using the unique strengths of the technologies that are now available.

A recent PwC survey found that, even among those adopting AI agents, fewer than half are fundamentally rethinking operating models and how work gets done (45%) or redesigning processes around AI agents (42%). [3]

Prepare for Rapid Fire Testing and Iteration

With no- and low-code agent platforms, it has become much easier to prop up an agent and start testing it. Momentum is critical to the maturation of AI agents, and the initial goal isn’t to build the perfect AI agent, it’s to build a prototype that can be run through a gauntlet of testing and iteration.

In the ideal scenario, this phase of AI agent development involves logging agent actions, tracking performance, and setting up alerts for failures. By creating pipelines for testing and deployment organizations can unlock the rapid iteration required to create AI agents that deliver real return on investment (ROI). By starting small, validating design choices, and then scaling efforts. As you are designing your first AI agent, it’s important to document workflows and roles for maintenance. It can also be helpful to leverage open-source communities for support and best practices.

As Gartner analyst Daniel Sun instructs, “Evaluate the outcomes. Assess the impact of the AI-agent-powered innovations. Track KPIs (key performance indicators) and evaluate how well these AI solutions are meeting initial goals. Refine the strategies based on this data.”

From First AI Agent to Full Ecosystem

For anyone leading efforts to introduce AI agents into an organization, building your first agent is the first step in what will become a long and far-ranging journey. There are a variety of platforms and frameworks available to help teams build AI agents. It can be as easy building an agent around familiar use cases with existing templates. An agent building can also be as in-depth as creating a custom framework using Langchain and Python, which is a flexible approach but one with a hefty learning curve.

Agent platforms give orgs the opportunity to quickly deploy and test potential automations with no- and low-code tools that let anyone with an idea about automation contribute to the design of an emerging agentic system. Platforms such as GSX enable groups to accelerate the early stages of agent development while laying a foundation for growth that allows scaling both horizontally (adding more instances as demand increases) and vertically (upgrading resources for agents using more CPU (Central Processing Unit)).

OneReach.ai’s GSX Agent  Platform allows organizations to streamline AI agent deployment, and enable seamless interaction with necessary tools and systems. It empowers organizations to undertake Agent Lifecycle Management: starting from design, training, and testing to deployment, monitoring, and optimization of intelligent multimodal agents.