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I worked on NeuralSeek’s mAIstro platform, a no-code environment for building multi-step, multi-model AI agents. Designing a 13-node pipeline agent that turns a plain-English automation idea into grounded, source-backed recommendations for how a user might design their own agent. The agent chains a sequence of LLM calls, then runs a variable loop that issues Seek queries against a curated knowledge base before returning its results.
Building the agent end to end taught me how to think about agent architecture rather than just individual prompts. After scraping and restructuring NeuralSeeks’s documentation into roughly twenty focused knowledge base files, setting up category routing to separate between types of sought questions and configuring guardrails I saw how real queries behaved. Much of my deeper learning came from debugging NeuralSeek Template Language where I worked through the difference between chained and stacked node execution, figured out how variables parse and carry between nodes, and sorted out parallel execution grouping so the loop ran in the right order.
Overall, the project gave me a better understanding of how to build and debug AI agents from the ground up. It also gave me hands-on experience with retrieval-augmented grounding, prompt design, knowledge base organization, and iterative debugging. Most importantly, I learned how much of building an AI system comes from testing, finding what breaks, and simplifying the design until it works the way it should. I hope to carry that experience into future work in AI and software development.