Future Trends and Conclusion
Understand how the skills learned in this course position you at the forefront of AI development and what your next steps can be.
In our last lesson, we took a crucial look at the real-world challenges of deploying an agentic system. We’ve now walked the full path: from a basic RAG concept to designing, building, debugging, evaluating, and finally, understanding the immense challenges of deploying an agent in the real-world.
In this concluding lesson, we will first look back at the journey we’ve traveled to consolidate our knowledge. Then, we will look to the horizon and discuss the “what’s next,” the exciting future trends that are shaping the next generation of agentic AI.
A course recap: The skills in your toolbox
It’s worth taking a moment to appreciate the ground we’ve covered. We began with a simple problem, the limitations of standard RAG, and progressively built a sophisticated, multi-tool agent to solve it.
Throughout this journey, you have acquired a set of concrete, high-value skills that are essential for a modern AI engineer.
AI architecture: You can design an agentic system from scratch, defining its requirements and mapping them to tools.
Practical implementation: You can build custom tools and orchestrate them with a
ReActAgentusing LlamaIndex.Debugging and refinement: You know how to analyze an agent’s trace to diagnose failures and improve its reliability.
Evaluation strategy: You understand how to measure an agent’s performance quantitatively.
Production mindset: You are aware of the security, cost, and reliability challenges that must be solved to deploy a real-world application.
The near future
The field of agentic AI is moving at an incredible pace. The concepts we are about to discuss are not science fiction; they are active areas of research and development that are already emerging in advanced systems today.
Trend 1: The rise of multi-agent systems
We touched on this in our lesson on Agentic ...