Learn how to build AI agents efficiently with RubikChat. Follow our step-by-step guide on AI agent development using an AI agent builder to automate tasks, reduce manual work, and create reliable, domain-specific agents.
AI agents are software systems designed to handle tasks that involve multiple manual steps. These tasks often need context, judgment, and adaptability—making them hard to automate with basic rule-based code.
Traditional automation is possible, but it usually involves hardcoding countless edge cases. RubikChat offers a smarter alternative. It supports AI agent development that uses context to decide the next step, cutting down manual effort while leaving important decisions open for review.
The most effective AI agents are narrow, focused, and domain-specific. Here’s how to approach building one.
Before writing a single line of code, start by simulating the agent manually.
Break the task down as a human would, step by step. Use real inputs—screenshots, API responses, messy CSVs—and feed them into an LLM with prompts that mirror the actual workflow. Perform the actions yourself as you go, and pay attention to steps that feel repetitive or mechanical. These are prime candidates for automation later.
With RubikChat, a no-code AI agent builder, you can later automate these steps without writing code.
If the model consistently fails even with adjustments, the task may not suit an AI agent. But if it works—or even comes close—it’s worth continuing.
Once your manual simulation shows the task is feasible, you can start building the agent. With a no-code AI agent development, you don’t need to write traditional code to create the agent skeleton.
Automate input collection—APIs, scrapers, screenshots, or any other source—and define the agent’s workflow as a loop or simple state machine: collect input → perform deterministic steps where possible → call the model for judgment → evaluate results → decide whether to continue or exit.
Remember, LLMs are non-deterministic. If a step can be handled with a normal function, let it. Use plain automation for parsing, calculating, or sorting—reserve the AI model for steps that require reasoning.
Building AI agents isn’t about reinventing programming. With RubikChat, you can structure workflows, loops, and conditional logic visually, without overcomplicating things. Your agent is ready when it reliably produces acceptable results with minimal intervention.
Once the agent is running end to end, shift your focus to improving its quality. With RubikChat, you can refine workflows and optimize performance without writing code. Ensure the agent performs reliably on both specific examples and a wide range of real-world inputs.
Tighten the loop: improve prompts, make tool calls precise, eliminate unnecessary retries, and replace AI model calls with deterministic steps wherever possible. If results are incomplete, a second model can be used to critique and continue the work.
Hands-on testing and intuition remain key. Iterate until the agent feels robust across real examples. Then, implement structured evaluations to measure performance across diverse inputs and edge cases. This approach ensures consistent quality as your AI agent evolves and scales.
AI agent development expands what software can do, but building one doesn’t require reinventing programming. The most reliable agents are grounded in clear logic, solid structure, and tight feedback loops.
Test the task manually first with real inputs and prompts. If the model gets close with guidance, it’s worth building. Structure the workflow with ordinary code and rely on the model only when necessary. Once the agent is functional, focus on making it resilient across individual inputs and real-world complexity.
AI agents shine when:
Traditional automation is too brittle or complex
Manual prompting shows the model can succeed
The agent is tightly scoped and built on solid coding principles
Optimization combines hands-on intuition with structured evaluation
When done right, AI agents feel magical—but it’s not magic. It's a smart system with reasoning built in. RubikChat solid AI agent builder can help you achieve this efficiently.