Building AI Agents

Building AI agents is very different from building traditional applications. In a conventional app, you define inputs and outputs, wire together some logic, and expect the system to behave predictably. With agents, the goal is to create an autonomous system that can perceive its environment, reason about information, and act to achieve objectives without requiring every step to be explicitly defined. This is not just another layer of automation—it is a shift in how software is conceived and built.

From Programs to Problem-Solvers

The idea of an AI agent is to move beyond reactive software. Chatbots, for example, are reactive: they wait for a prompt, then provide an answer. An agent, by contrast, is proactive. It can be given a goal, such as “plan a trip,” “research competitors,” or “monitor customer support emails,” and then take initiative. It will decide which steps to take, what information to gather, and how to adapt when conditions change. The key difference is that agents do not stop after one response—they run in loops until the task is complete, checking their own work and adjusting as needed through sophisticated agent workflows.

The Challenges of Building Agents

Creating agents that actually work in practice is difficult. Unlike short-lived web requests, agents are often long-running processes. They need to maintain state across multiple steps, remember prior context, and work with a variety of inputs and outputs. They must be able to pause and resume, to recover gracefully from failures, and to interact with external tools or APIs without brittle wiring. Most traditional infrastructure was never designed for this kind of workload. Serverless platforms impose timeouts, web-native services assume statelessness, and monolithic frameworks force developers into rigid patterns that don't map well to the fluid nature of agents—which is why AI-native infrastructure has become essential.

Building Without Lock-In

Because the field of AI agents is still so young, flexibility is critical. Different teams experiment with different approaches—TypeScript with one orchestration library, Python with another, or entirely custom-built runtimes. No single framework has “won,” and it’s unlikely that one ever will. That is why at Agentuity, we built our platform to be language and framework agnostic. Developers can bring their own tools and runtimes—Bun, Python, Node.js, or whatever else they prefer—and deploy agents in a consistent environment without rewriting code to fit into someone else’s opinions.

This is more than a convenience. It means developers can focus on experimenting with the behaviors of their agents rather than fighting with infrastructure. It means projects don’t get locked into fragile toolchains that may be abandoned in a year. And it means teams can compose agents written in different languages into the same system, letting them collaborate seamlessly as peers.

Treating Agents as First-Class Systems

An agent is not a background script or a one-off Lambda function. It is a first-class system that requires observability, memory, and robust lifecycle management. When you build on Agentuity, your agents are treated this way from the start. They can run for as long as they need, they can be paused and resumed, and they can handle diverse forms of input—from JSON to emails to scheduled events. Observability is built into the platform, not bolted on afterward, so both developers and agents themselves can trace reasoning and improve performance over time.

Why Agentuity

Most developers who try to build agents on hyperscale cloud platforms eventually run into the same problems: timeouts, brittle networking, lack of state, and rigid frameworks that don’t allow experimentation. We built Agentuity precisely to solve those issues. Our platform was designed for agents, not for websites, and that difference shows in how much faster teams can move from prototype to production. With Agentuity, developers get the freedom to build however they want, the reliability of infrastructure tuned for long-running, stateful systems, and the ability to connect agents together as if they were natural components of the same project.

Building AI agents is the future of software development. But building them on infrastructure that was never designed for them is like trying to run a marathon in shoes two sizes too small—you can do it, but it’s painful and slow. Agentuity gives developers the right foundation so that building agents feels natural, scalable, and sustainable. That’s what it means to build for the agent-native era.