⬅️ Previous - Program Curriculum
➡️ Next - Workflows to Agentic Systems Preview
This program is for developers ready to build AI systems where multiple agents coordinate to solve complex problems—like a team of specialists working together.
Ideal for:
⚠️ Note: This program assumes you’re comfortable with Python and have some experience working with LLM APIs or AI applications. If you’re brand new to building with AI, start with Agentic AI Essentials.
You can, but the program moves quickly into multi-agent coordination and assumes familiarity with basic agent concepts like prompts, tools, and state.
If you’re completely new to agentic AI, we recommend starting with Agentic AI Essentials first. If you’ve already worked with LLM APIs and simple agents, you should be able to follow along.
No formal prerequisite is required. However, prior experience with RAG systems or LLM-based workflows will make this program smoother, since multi-agent systems build on those foundations.
No formal background in data science or machine learning is required. You should be comfortable with programming and have some exposure to APIs or LLM-based systems.
You should be comfortable with intermediate Python, including functions, classes, control flow, and working with libraries. Experience with async code is helpful but not required.
You’ll need:
Most learners complete the program in 3–6 weeks, depending on pace, prior experience, and project complexity.
The learning is fully self-paced. Project submissions are reviewed in monthly cycles, but you can submit when you’re ready.
Most learners spend 25-40 hours total completing the lessons and multi-agent project. Time varies based on:
Multi-agent systems require more planning and debugging than single-agent projects.
You’ll need an LLM API key. Most learners stay within free or very low-cost tiers, though multi-agent systems may generate more API calls during testing.
Yes. The program is designed so you can complete it using free-tier or low-cost tools, with reasonable API usage.
Yes. Using your own multi-agent use case is encouraged, as long as it demonstrates agent coordination and meets the project requirements.
Yes. LangGraph is recommended because it excels at stateful, graph-based workflows, but you may use alternatives such as:
Projects are evaluated on multi-agent design and coordination, not the specific framework used.
Yes, as long as the multi-agent work completed during this program is clearly documented and aligns with the project requirements. If others contributed, properly acknowledge their work and clearly describe your role.
You’ll receive detailed feedback on your system design and coordination approach and can revise and resubmit in a future review cycle.
Yes. Evaluation focuses on your architecture, coordination strategy, and reasoning, not flawless performance. Iteration is expected for real-world multi-agent systems.
You can ask questions in the Ready Tensor community and Discord channels, where peers and mentors help with multi-agent design and implementation challenges.
This program focuses on multi-agent system design and orchestration. Other Agentic AI programs cover foundational RAG systems or production deployment, testing, and monitoring.
Yes. Each program is self-contained and can be taken independently, though this program provides strong system-design foundations.
You earn the Agentic AI Builder certificate and a shareable digital badge.
Yes. Your completed multi-agent project is designed to be portfolio-ready and publicly shareable.
Common next steps include production deployment, testing and evaluation of agentic systems, or applying multi-agent patterns to real-world domains like customer support, research, and automation.
This program prepares you for roles involving AI system design and orchestration, such as AI/ML Engineer or AI Product Engineer positions working on multi-agent workflows, task delegation, and intelligent automation. Additional production-focused skills are covered in other Agentic AI programs on Ready Tensor.
⬅️ Previous - Program Curriculum
➡️ Next - Workflows to Agentic Systems Preview
