AI Agents & Orchestration
Understand agents as model-driven workflows that plan, call tools, and complete multi-step tasks.
Agents combine model decisions with tools, state, and a runtime loop. These lessons focus on boundaries: when autonomy helps, how tool calls are validated, and how to keep multi-step work observable and recoverable.
Autonomous AI Agents Explained
Learn what makes an AI agent different from a single model call or chatbot response.
10 min read →LangChain vs. LlamaIndex
Compare two popular ecosystems for building model-powered applications and agent workflows.
10 min read →Building Tool-Calling Agents
Design safe tool schemas and execution loops for agents that need to interact with software.
11 min read →Multi-Agent Systems
Learn when multiple specialized agents improve a task and when they only add complexity.
10 min read →Agent Memory Management
Design short-term and long-term memory that helps agents without accumulating unreliable state.
10 min read →