Learning Objectives & Key Takeaways
0 / 4 Completed- Understand OpenAI core architecture and runtime execution model
- Audit real-world latency, throughput benchmarks, and memory footprint
- Implement zero-downtime deployment patterns with resilience guards
- Verify code snippets and architect blueprints against production SLOs
- The Agents SDK defaults to gpt-5.6-luna with reasoning effort none unless you set a model explicitly.
- tool_search only works on gpt-5.4 and later models, so check support before wiring it in.
- The Responses API makes you own the tool loop; the Agents SDK runner owns it for you.
- Built-in tools, function tools, tool search, PTC, and remote MCP servers are five separate tool paths.
- OpenAIResponsesModel is the Agents SDK's recommended OpenAI wrapper, not Chat Completions.
The Silent Downgrade
A support team shipped a customer-facing agent built on the Agents SDK and assumed it was running their strongest reasoning model, since nobody had typed a model name anywhere in the agent definition. Within a week, billing disputes that needed careful multi-step reasoning were getting flat, one-line answers that missed obvious context from earlier in the conversation.
The on-call engineer pulled the request logs and found the model field quietly set to gpt-5.6-luna, with reasoning.effort set to none and verbosity set to low. Nothing was broken. This is the documented default for an Agents SDK agent that never specifies a model, and it is tuned for cost-sensitive, high-volume workflows rather than nuanced customer escalations.
- Pick Your Control Boundary First
- Models Have Opinions by Default
- Five Doors Into Tool Calling
- A Complete Agents SDK Example
- Owning the Loop Yourself with the Responses API
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