Agent Mesh

A multi-agent structure for planning, delegating, reviewing, and recovering without letting one model own every decision.

One model should not do every job. The winning architecture uses specialisation and feedback loops.

Delegated workStructured handoffRecovery paths

What this proves

Split planning from execution and review from completion.

Let specialists own narrow tasks and return structured results.

Use memory and handoff boundaries so the workflow can resume cleanly.

Open-source stack

LangGraphLangChainLlamaIndexRedisPostgresReview gates

Experience mode

Step 1

Request

Step 2

Planner

Step 3

Task graph

Step 4

Plan

Planning layer

Planning is the difference between a conversation and a workflow.

Live pattern

Engineering lens

The planner creates a map before any tool is used.
Small jobs make progress visible.
Success criteria keep the model honest.

Platform fit

This project belongs in Llewellyn Systems because it turns a repeated engineering pattern into a governed operating asset. The page is not a slide deck. It is a proof surface for how the system is built and how it behaves.

Toolchain note

Use JupyterBook for publication, MyST for source text, Voilà for notebook apps, Binder for reproducible environments, and JupyterLab or Colab for interactive editing. The page itself is the front door to that workflow.

Related projects

Skills + MCP