Path 1
Builders
Engineers building agents
You're hands-on with LLMs and agents. Start with the primitives framework, then learn orchestration patterns and tool integration.
Learning paths
Articles
1
Building Your First AI Agent: A Practical Introduction
agent-fundamentals
→
2
Agentic Primitives: The Building Blocks of AI Agent Systems
agent-fundamentals
→
3
Tasks, Skills, and Tools: The Holy Trinity of AI Agents
agent-design
→
4
MCP Deep Dive: What the Model Context Protocol Actually Does (And Doesn't Do)
protocols-standards
→
Path 2
Architects
Designing agent systems
You're designing how agents fit into enterprise systems. Start with the protocol landscape, then dive into API design and integration patterns.
Learning paths
Path 3
Leaders
Governance & strategy
You're responsible for AI strategy, risk, and governance. Start with understanding what agents actually are, then explore governance and reliability.
Learning paths
Articles
1
Agents Are Not Magic: Why the Hype Around 'Autonomous' AI Is Dangerous
agent-fundamentals
→
2
The False Economy of Speed: Why Skipping API Governance Leads to Agentic Technical Debt
governance-operations
→
3
Observability for Agentic Systems
governance-operations
→
4
Benchmarking Agent Reliability
research
→