We are a small AI & software studio exploring agentic systems, multi-agent decision making, and next-generation learning experiences. From research prototypes to production-ready tools, we help organisations take their first step into the agentic era.
Agentic Initial Studio is founded by researchers and engineers with experience in multi-agent systems, optimisation, and learning technologies. We bridge academic research and real-world applications.
We combine research in agent-based systems and reinforcement learning with practical software engineering. Our goal is to turn complex ideas into reliable, understandable systems.
We care about systems that are explainable, maintainable, and actually useful to people. That means careful design, transparent reasoning, and honest evaluation of what AI can and cannot do.
We are based in Australia and collaborate remotely with partners across timezones. We are comfortable working in English and Chinese contexts, especially across education and industrial domains.
We work with organisations at different stages — from early exploration and prototyping to integrating agentic AI into existing systems.
Design and prototype agentic workflows for logistics, scheduling, or decision-making tasks. We focus on clear state representations, robust coordination, and safe action spaces.
Build AI-assisted tools for teaching, assessment, and learner support: from smart exercises and feedback systems to interactive lab assistants and tutoring agents.
Need to test an idea quickly? We can build focused proof-of-concepts and demo systems that help you evaluate feasibility, gather feedback, and plan production-grade builds.
Here are typical directions where we can add value. Each engagement can be tailored to your data, constraints, and organisational context.
Design agents that reason about capacity, constraints, and routes to support human planners. For example, container loading, truck routing, or resource allocation with real-world constraints.
Build AI helpers that support students with code, explanation, and feedback, while keeping teachers in control. From programming labs to technical report feedback.
Use LLMs and structured agents to triage requests, summarise documents, or coordinate small workflows across APIs and internal systems, with a focus on reliability and auditability.
If you are interested in collaborations, prototypes, or simply want to discuss an idea, feel free to reach out. A short description of your context and goals is always helpful.
Typical collaboration flow: