Mastering AI Agentic Loops
Progress from raw ReAct cycles to stateful self-reflection, upfront planning, multi-agent hierarchies, and enterprise safety structures.
Introduction to the Agentic Loop
Build a self-contained Python agent that autonomously searches, evaluates, and summarizes a topic — tracking its own state and knowing when to stop.
Reflection & Self-Correction
Build an agent with a Generator and a Critic. The Critic scores the Generator's output; the loop continues until the score passes or the iteration budget is exhausted.
Dynamic Tool Use & Planning
Build a Plan-and-Execute agent: it first decomposes a complex goal into a task DAG, then executes each task with appropriate tools, re-planning when tool outputs contradict assumptions.
Multi-Agent Choreography vs. Orchestration
Build a 4-agent swarm: a Manager, a Research Analyst, a Code Writer, and a Code Reviewer. The Manager orchestrates the workflow; agents pass structured outputs to each other.
Productionizing Agentic Loops
Deploy a code-review agent with: human-in-the-loop approval for destructive actions, LangSmith tracing for every loop iteration, and a token budget dashboard.