Deep-Tech AI Trajectories

Agentic architecture is broader than simply using an LLM.

A practical Agentic AI system typically combines:

LLM + Reasoning + Planning + Tools + Memory + Observation + Action + Feedback

This makes ReAct, Plan-and-Execute, Reflection/Reflexion, Memory-Augmented, Hierarchical, Multi-Agent, Supervisor–Worker, and Human-in-the-Loop architectures particularly important topics for an AI Engineering course.

Deep-Tech topics with architectures in the sequence Tool-Calling → ReAct → Planning → Reflection → Memory → RAG + Agents → Multi-Agent Systems → Autonomous Agents, followed by hands-onprojects.

From 2020 onward, the AI landscape has evolvedrapidly from task-specific machine learning and generative models toward autonomous, reasoning-oriented AI systems capable of perceiving context, planning multi-step actions, using external tools, maintaining memory, collaborating with other agents, and operating within real-world environments. The emergence and rapid advancement of foundation models and LLMs has accelerated this transition toward Agentic AI. Deep knowledge in the following technology dimensions is particularly relevant to the development of modern Agentic AI systems:

Mamba and Jamba Architectures
Multi-step reasoning
Memory mechanisms
Tooluse
Multi-agent collaboration
Infrastructure and orchestration
Safety and security
BDI frameworks
Agentic RAG
Chain-of-Agents
Vision-language-actionmodels
Benchmarks: BIRD(Text-to-SQL), SWE-bench(real-world software engineering / bug-fixing), and Terminal-Bench (DevOps/live shell system administration)
Evaluation methodologies: Deterministic programmatic checks, LLM-as-a-judge trajectory analysis, and Human-in-the-loop validation