| Troubleshooting | L37-L42 | Diagnosing evaluation/observability problems in Foundry (metrics, logging, tracing) and resolving known platform issues with documented workarounds. |
| Best Practices | L43-L54 | Best practices for configuring tools, prompts, evaluation, safety, latency, and fine-tuning (incl. vision models) to build high-quality, efficient Azure AI/Foundry agents |
| Decision Making | L55-L85 | Guides for choosing models, deployments, costs, and tools, plus migration and upgrade paths (Azure OpenAI, GitHub Models, Assistants API) and web/Bing grounding decisions. |
| Architecture & Design Patterns | L86-L97 | Architectural patterns for Foundry agents: standard setup, RAG/indexing, HA/DR, regional recovery, provisioned throughput, spillover traffic, and LLM routing optimization. |
| Limits & Quotas | L98-L112 | Limits, quotas, regions, and availability for Foundry and Azure OpenAI models, agents, evals, vector/file search, batch, fine-tuning, and partner models. |
| Security | L113-L145 | Security, identity, networking, and compliance for Foundry: auth/RBAC, keys & encryption, private networking, guardrails, safety policies, content safety, and data privacy for models and agents |
| Configuration | L146-L192 | Configuring Foundry agents, models, tools, storage, monitoring, security, and Azure OpenAI features (search, memory, tracing, fine-tuning, prompt controls, and external integrations). |
| Integrations & Coding Patterns | L193-L257 | Patterns and code for integrating Foundry agents and models with tools, APIs, LangChain/LangGraph, Azure OpenAI, realtime/audio, search, safety, tracing, and enterprise systems. |
| Deployment | L258-L273 | Deploying agents and models: infra setup, hosting, publishing to Azure/M365/Teams, CI/CD, workflows, custom/fine-tuned/Fireworks models, and managing deployment lifecycle. |