In AI Agent services, user trust depends not only on the final answer but on how progress is shown during execution. This post compares SSE and WebSocket for token streaming, step status, tool execution events, and intermediate results, with practical guidance for real product teams.
OpenAI introduced the Responses API and Agents SDK on March 11, 2025. This post looks at why that announcement became a key architectural reference point for AI Agent products by 2026.
A practical guide to turning AI Agents into real services. Covers Tool Calling, Planner/Executor separation, session state management, human-in-the-loop workflows, failure handling, and cost control.
A practical blueprint for a RAG-based AI stock investment Agent. Covers product goals, user scenarios, system boundaries, core components, and end-to-end architecture for a research and paper-trading workflow.
A practical design for the workflow of an AI stock investment Agent. Covers routing, query parsing, screening, retrieval analysis, quantitative analysis, risk evaluation, and final report composition.
A practical operations guide for a stock investment Agent. Covers paper-trading workflow, human approval, monitoring, alerts, audit logs, failure handling, and the guardrails needed before any real execution.
How to design production AI Agent systems. A practical guide covering the ReAct pattern, Tool Use, Memory management, Multi-Agent orchestration, and safety design.