Hands-on Guide to Agentic Design Patterns

In today’s newsletter:

• SkyPilot - Scale AI Workloads Seamlessly Across Clouds and Clusters

• Hands-on Guide to Agentic Design Patterns

• LEANN - Turn your Laptop into a RAG Powerhouse.

Reading time: 3 minutes.

Run, Manage, and Scale AI workloads on Any Infrastructure!

SkyPilot is an open source framework that provides a unified, cloud-agnostic control plane for launching, monitoring, and optimizing AI workloads.

With SkyPilot, you can train, fine-tune, serve, run batch jobs, and process data across 16+ clouds, local GPUs, and Kubernetes.

It removes the need to rewrite code or reconfigure for each environment.

Key Features:

• Unified execution across 16+ clouds, local GPUs, and Kubernetes (including AI-optimized Neoclouds)

• Cost optimization with spot instances, auto-recovery, and idle cleanup

• Slurm-like job management with queuing, scheduling, and log streaming

• Flexible hardware provisioning for GPUs, TPUs, and CPUs

• Simple workflows using YAML or Python API

It’s 100% Open Source.

👉 Check out the Github repo

Hands-on Guide to Agent Design Patterns

Agent frameworks are everywhere, but practical design resources are rare.

Antonio Gulli from Google released a free 424-page book on Agentic Design Patterns. It is packed with system-level insights and hands-on code.

This is one of the most comprehensive resources available for building real-world agents.

Here’s what it covers:

• Prompt chaining, routing, parallelization, reflection, planning, and tool use.

• Multi-agent design, memory management, learning and adaptation, and MCP.

• Goal setting, exception handling, human-in-the-loop, and retrieval (RAG).

• Inter-agent communication, resource optimization, reasoning techniques, and safety patterns.

• Evaluation, monitoring, prioritization, exploration, and discovery.

• Advanced prompting, frameworks overview, coding agents, and reasoning engines under the hood.

👉 Check out the Guide

Turn your Laptop into a Powerful RAG System

Most personal RAG setups break down at scale.

LEANN is a lightweight, privacy-first retrieval system that indexes and searches millions of documents while using 97% less storage than traditional solutions without losing accuracy.

It avoids storing all embeddings by using graph-based selective recomputation with high-degree preserving pruning and computes embeddings only when needed.

Key Features:

• Privacy-first: Everything runs locally, no cloud dependencies.

• Lightweight: Graph pruning + CSR format minimize storage and memory.

• Portable: Easily move knowledge bases across devices.

• Scalable: Handles messy personal and agent-generated data.

• Accurate: Matches heavyweight vector DBs while using a fraction of the storage.

It’s 100% open-source.

👉 Check out the Github Repo

That’s a Wrap

That’s all for today. Thank you for reading today’s edition. See you in the next issue with more AI Engineering insights.

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