<?xml version="1.0" encoding="UTF-8"?><rss version="2.0"><channel><title>Multi-Agent LLM Systems - Penluma</title><description>Foundations, patterns, protocols (MCP/A2A), memory, reliability, and cost of agentic systems.</description><link>https://penluma.com/</link><item><title>Multi-Agent AI Systems: When (and When Not) to Use Them</title><link>https://penluma.com/blog/agent-orchestration/agent-orchestration-00-index/</link><guid isPermaLink="true">https://penluma.com/blog/agent-orchestration/agent-orchestration-00-index/</guid><description>Multi-agent AI systems can cost 15x more tokens than one chat turn. Learn when agent orchestration actually pays off and when a single prompt wins.</description><pubDate>Tue, 16 Jun 2026 00:00:00 GMT</pubDate><category>Multi-Agent LLM Systems</category></item><item><title>What Is an AI Agent? (And When You Actually Need One)</title><link>https://penluma.com/blog/agent-orchestration/agent-orchestration-01-foundations/</link><guid isPermaLink="true">https://penluma.com/blog/agent-orchestration/agent-orchestration-01-foundations/</guid><description>Learn what an AI agent really is, how the agentic loop works, and when to choose a plain LLM call, a workflow, or a multi-agent system instead.</description><pubDate>Tue, 16 Jun 2026 00:00:00 GMT</pubDate><category>Multi-Agent LLM Systems</category></item><item><title>Multi-Agent Orchestration Patterns: It&apos;s the Wiring</title><link>https://penluma.com/blog/agent-orchestration/agent-orchestration-02-patterns/</link><guid isPermaLink="true">https://penluma.com/blog/agent-orchestration/agent-orchestration-02-patterns/</guid><description>A plain-language field guide to multi-agent orchestration patterns: prompt chaining, routing, orchestrator-worker, swarms, debate, and when to use each.</description><pubDate>Tue, 16 Jun 2026 00:00:00 GMT</pubDate><category>Multi-Agent LLM Systems</category></item><item><title>How AI Agents Talk to Each Other: MCP, A2A &amp; Handoffs</title><link>https://penluma.com/blog/agent-orchestration/agent-orchestration-03-communication-protocols/</link><guid isPermaLink="true">https://penluma.com/blog/agent-orchestration/agent-orchestration-03-communication-protocols/</guid><description>A plain-English guide to how AI agents share state, hand off control, and use tools - covering MCP, A2A, handoffs, and structured outputs that actually work.</description><pubDate>Tue, 16 Jun 2026 00:00:00 GMT</pubDate><category>Multi-Agent LLM Systems</category></item><item><title>AI Agent Frameworks in 2026: Which One Should You Use?</title><link>https://penluma.com/blog/agent-orchestration/agent-orchestration-04-frameworks/</link><guid isPermaLink="true">https://penluma.com/blog/agent-orchestration/agent-orchestration-04-frameworks/</guid><description>A plain-language guide to AI agent frameworks like LangGraph, CrewAI, and the OpenAI and Claude SDKs, and how to pick the right one without over-engineering.</description><pubDate>Tue, 16 Jun 2026 00:00:00 GMT</pubDate><category>Multi-Agent LLM Systems</category></item><item><title>Context Engineering for AI Agents: Why More Isn&apos;t Better</title><link>https://penluma.com/blog/agent-orchestration/agent-orchestration-05-context-memory/</link><guid isPermaLink="true">https://penluma.com/blog/agent-orchestration/agent-orchestration-05-context-memory/</guid><description>Context engineering for AI agents means feeding the model the smallest set of high-signal tokens. Learn why more context hurts accuracy, cost, and speed.</description><pubDate>Tue, 16 Jun 2026 00:00:00 GMT</pubDate><category>Multi-Agent LLM Systems</category></item><item><title>Why AI Agents Fail on Long Tasks (and How to Catch It)</title><link>https://penluma.com/blog/agent-orchestration/agent-orchestration-06-reliability-eval-obs/</link><guid isPermaLink="true">https://penluma.com/blog/agent-orchestration/agent-orchestration-06-reliability-eval-obs/</guid><description>AI agents that do long, multi-step work fail in ways single prompts never do. Learn why reliability decays, how to evaluate agents, and how to see inside them.</description><pubDate>Tue, 16 Jun 2026 00:00:00 GMT</pubDate><category>Multi-Agent LLM Systems</category></item><item><title>Why Multi-Agent AI Costs 15x More (And When It&apos;s Worth It)</title><link>https://penluma.com/blog/agent-orchestration/agent-orchestration-07-cost-performance/</link><guid isPermaLink="true">https://penluma.com/blog/agent-orchestration/agent-orchestration-07-cost-performance/</guid><description>Multi-agent AI systems burn about 15x the tokens of a normal chat. Learn what drives the cost, when it pays off, and how to cut costs without losing quality.</description><pubDate>Tue, 16 Jun 2026 00:00:00 GMT</pubDate><category>Multi-Agent LLM Systems</category></item><item><title>How AI Really Works Inside a Print Shop SaaS (Case Study)</title><link>https://penluma.com/blog/agent-orchestration/agent-orchestration-08-applied-case-study/</link><guid isPermaLink="true">https://penluma.com/blog/agent-orchestration/agent-orchestration-08-applied-case-study/</guid><description>See how AI agent orchestration really ships in a print-shop SaaS: what works today, the four constraints that block multi-agent flows, and where to grow next.</description><pubDate>Tue, 16 Jun 2026 00:00:00 GMT</pubDate><category>Multi-Agent LLM Systems</category></item></channel></rss>