Practical patterns for designing, orchestrating, and deploying AI agents in production. This section covers how agents use tools, how to structure multi-step workflows, and how to keep agent behaviour predictable at scale.
What you will find here
- Tool use — how language models call functions, when to use tools versus prompting, and how to handle tool errors gracefully.
- Orchestration patterns — sequential chains, parallel fan-out, conditional branching, and when to use each.
- Memory systems — short-term context management, long-term memory stores, and retrieval-augmented memory.
- Multi-agent coordination — routing, delegation, shared state, and avoiding coordination failures.
- Reliability — retries, timeouts, fallbacks, and testing agent behaviour before deployment.
All articles focus on what works in production, not theory. Where trade-offs exist, they are stated explicitly.