Demystifying Agentic AI Using Small Language Models

The Agentic Buzz - What’s Real, What’s Marketing

• The explosion of “agentic” frameworks and the confusion it causes
• What an agent really is at its core: planning, acting, and reasoning

Anatomy of an Agent

• The three basic functions: task decomposition, tool use, and code synthesis
• How frameworks like LangChain and Python make it easy to chain these together

Why Small Models Are Catching Up - Review of research from NVIDIA and Georgia Tech - Benchmarks showing SLMs matching or exceeding performance of larger LLMs - Cost, latency, and deployability tradeoffs

Hands-On Demo: Building and Running an Agent on a Laptop

• Using LangChain and Python to orchestrate reasoning, tool calls, and code execution
• Example workflow: “Plan a dataset cleanup pipeline” using an SLM
• Observing resource use, latency, and performance in real time

Key Takeaways and Open Research Directions

• Opportunities for local and edge deployments
• The emerging role of SLMs in allowing everyone to experiment with agents
• Future questions: scaling reasoning vs. scaling models