Memory systems, multi-agent orchestration, RAG pipelines, and production-grade agent infrastructure — the advanced architecture skills for engineers who are past the basics.
This pathway assumes you already understand AI agent basics (what loops, tools, and harnesses are) and are ready to tackle production concerns: memory systems that persist across sessions, agentic RAG pipelines for dynamic knowledge retrieval, context compression for long-running agents, multi-agent orchestration patterns, and prompt caching for cost optimization at scale.
Multi-agent orchestration is the design of systems where multiple AI agents collaborate to complete tasks too large or complex for a single agent. Common patterns include orchestrator/worker (one agent coordinates many), pipelines (agents pass work sequentially), fan-out (parallel specialist agents), and debate (agents challenge each other's outputs). This pathway covers all major patterns with production implementation guidance.
11 articles, approximately 8 hours. This is the deepest technical pathway on the platform and is recommended after completing Building AI Agents.
Understand what AI actually is — tokens, transformers, agents, and the landscape. Start here if you're new.
11 articles · ~4h →Go from vague requests to precise, reproducible AI outputs. The skill that underpins everything.
13 articles · ~5h →Go from zero to productive with Claude Code — the terminal AI coding agent that ships real projects.
15 articles · ~7h →What Is MEMORY.md? Long-Term Brain for AI Agents
How agents maintain state and context across sessions.
Karpathy LLM Wiki: The Pattern Behind Agent Memory
Andrej Karpathy's approach to building persistent agent memory.
What Is an Obsidian Vault? Viral Graph Post Fact-Checked
Debunking the Anthropic leak hype — vault anatomy, graph view, and self-writing agent setups.
RAG vs Agentic RAG: Why Search Beats Embeddings for Code
When to move beyond naive RAG to agentic retrieval.
Langflow: Build Visual RAG Pipelines and Multi-Agent Workflows
Visual orchestration of complex agent pipelines.
Headroom: Context Compression for AI Agents
Keep agents effective even when context windows fill up.
Prompt Caching: LLM Cost, Latency, and Security Framework
Cache prompts intelligently to cut costs without sacrificing freshness.
Self-Harness: AI Agents That Improve Their Own Framework
The research pushing toward self-improving agent scaffolding.
Search as Code: Rethinking Search for the Agentic Era
How agentic search differs from keyword retrieval.
CocoIndex: Incremental Indexing for Always-Fresh Agent Context
Keep agent knowledge bases in sync without full reindexing.
Multi-Agent Orchestration Patterns
Orchestrator/worker, pipelines, fan-out, debate — the five patterns for production agent systems.
Error Propagation in Multi-Agent Systems
Structured error context over generic failure strings — enabling intelligent coordinator recovery instead of silent failures.
From AGI to ASI: DeepMind's 4 Pathways
The 57-page roadmap for what comes after human-level AI.
Understand and build the loops, harnesses, and protocols that make AI agents reliable and autonomous.
16 articles · ~6h →
