Learning Objectives & Key Takeaways
0 / 4 Completed- Understand Agentic AI 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
- An agent is a loop that plans, acts, observes, and repeats until done, not a single tool call.
- OpenAI offers three building blocks: Agents API, Agents SDK, and Responses API, each with a different control boundary.
- Memory means retained context, and each product stores it differently: managed sessions, SDK storage, or manual history.
- Approval belongs at the boundary where a model-proposed action becomes a real, external effect.
- Tool search lets you defer loading tool definitions instead of cramming every tool into every call.
The Night the Agent Refunded a Customer Twice
A support bot at a Bangalore based ecommerce startup had a single function tool wired to the payments API, and during a live demo it issued a refund, saw an ambiguous follow up message from the customer, and issued the refund again before anyone could react. The team had built a model that could call a tool, but they had not built a loop that observed the result of the first call before deciding whether a second call was even necessary.
Everyone on the call used the word agent, but nothing in the system actually behaved like one. There was no step where the model checked the outcome of its own action, no record of what had already happened in this conversation, and no gate that stopped a money moving action from firing without a human glancing at it first.
- What Actually Makes a System Agentic
- Pick Your Control Boundary: Agents API, Agents SDK, or Responses API
- Tools, Memory, and Where State Actually Lives
- Guardrails and the Human Approval Boundary
- A Minimal Agent You Can Actually Run
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