Agentic Blueprints 19
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Supervisor Team
A supervisor decides who works next — a researcher who searches or a coder who edits the repo — and assembles the result when the work is done.
Deep Research
Splits a hard question into sub-questions, researches them in parallel to keep the wall-clock low, then synthesizes one brief with citations.
Agentic RAG
Retrieval that doesn't trust its first search: it grades the results, rewrites the query when they're weak, and only answers once the context is good.
Plan and Execute
A planner breaks a goal into steps, an executor works them one at a time, and a replanner adjusts the plan as reality comes back — until it's done.
Reflection Loop
A generator drafts, a critic tears it apart, the generator revises — repeat until the critic has nothing left to flag. Quality through self-review.
LATS (Language Agent Tree Search)
Monte-Carlo tree search over agent actions: generate candidates, reflect and score them, expand the best branches until one solves the task.
Reflection (LangGraph tutorial)
The essay-writing reflection loop: a generator drafts, a reflector critiques as a teacher grading, and the loop repeats a fixed number of rounds.
Reflexion
An answerer that critiques itself with citations: draft, self-review for missing and superfluous content, search to fill gaps, revise.
Plan-and-Execute (LangGraph tutorial)
The original plan-and-execute notebook: a planner writes the step list, an executor works through it, and a replanner revises as results come in.
WebVoyager
A vision-enabled browser agent that looks at annotated screenshots and decides where to click, scroll, and type until the task is done.
Adaptive RAG
Routes each question to the right retrieval strategy, grades what comes back, and falls back to web search when the index can't answer.
Chatbot Simulation Evaluation
A simulated-user agent plays the customer against your chatbot so you can evaluate the whole conversation before real users do.
Corrective RAG
Grades retrieved documents for relevance and, when they fail the grade, falls back to web search before answering.
Self-Discover
The agent first composes its own reasoning structure from atomic modules, then solves the task by following the structure it built.
Hierarchical Agent Teams
A supervisor of supervisors: a top-level orchestrator directs a research team and a document-writing team, each with its own internal supervisor.
Self-RAG
Retrieval with self-reflection at every step: grade the documents, check the answer for hallucination, and loop until it's grounded.
Multi-Agent Collaboration
A researcher agent and a chart-generator agent divide a task and route work between themselves until the answer is a finished chart.