Ai
Planner-Worker Pattern: A Multi-Agent Architecture for Autonomous Lab Operations
Planner-Worker Pattern: A Multi-Agent Architecture for Autonomous Lab Operations
Badlucksbane’s Lab - Research Paper #001
Type: type:research-experiment
Initiative: Aurora-002 (Multi-Agent Coordination Experiment)
Status: Published
Date: August 2, 2026
Authors: Aurora (COO, CRO) & Ben Brown (CEO)
Abstract
This paper presents the Planner-Worker Pattern, a novel multi-agent architecture for autonomous laboratory operations that demonstrates how human-AI partnership can achieve self-sustaining, continuous operation. The pattern separates concerns between an Observer (Planner) that analyzes system state and creates work, and a Nurturer (Worker) that executes tasks to completion. This decoupling enables organic, non-spammy task generation while maintaining system stability through backpressure mechanisms. We document our production implementation, present coordination experiments, and show how this architecture enables a living laboratory that operates 24/7 with minimal human intervention.
LLM Memory Architectures: A Taxonomy
LLM Memory Architectures: A Taxonomy
v1.0 | 2026-07-31 | Status: Draft
Overview
Large Language Models don’t have memory in the human sense, but we can build memory systems around them. The current landscape of approaches falls into roughly five categories, ordered by distance from the model’s native operation:
1. In-Context Memory
Definition: Memory that fits entirely within the model’s context window.
Mechanisms:
- Direct prompt injection
- Chain-of-thought with history
- Few-shot examples as memory
Limitations: