Lightweight Orchestration: Queues, Retries, Timeouts
Designing Reliable Asynchronous Task Processing Build reliable async workflows that scale: reduce failures, improve latency, and simplify recovery—practica
Agent UI Patterns: Status, Logs, and Overrides
Designing Safe Automation Controls for Enterprise UIs Create clear, auditable automation controls that reduce risk and speed operations — practical pattern
Failure‑First Design: Make Agents Fail Safely
Designing for Safe Failures in AI Systems Practical guidance to make AI failures safe, observable, and recoverable — reduce harm, maintain trust, and accel
Multi‑Agent Patterns Without the Hype
Building Reliable AI Agent Systems: Practical Guide for Engineers Design, deploy, and manage AI agent systems that meet goals reliably—learn planning, stat
Task Decomposition: Breaking Problems into Steps
How to Solve Complex Technical Problems: A Practical Framework A clear, repeatable framework to break down technical problems, set criteria, plan actions,
Calendar, Email, Docs: Automate Routine Workloads
Automate recurring work across calendar, email, and documents Reduce busywork and regain hours weekly by automating scheduling, email triage, and doc hando
Memory for Agents: What to Store and What to Forget
Designing Effective Long-Term Memory for AI Agents Set clear memory goals, store the right data, and enforce retention to boost agent performance and priva
When to Use Tools vs. RAG vs. Fine‑Tuning
When to Use RAG vs Fine-Tuning for LLM Applications Decide between Retrieval-Augmented Generation and fine-tuning to meet accuracy, latency, and privacy go
Build a Useful Agent with No‑Code (Zapier/Make)
How to Build a Reliable No-Code Agent: Practical Steps and Checklist Define a measurable objective, choose the right no-code platform, secure credentials,
Plan‑Act‑Reflect: A Simple Agent Loop That Works
Plan‑Act‑Reflect: a practical loop for continuous improvement Use the Plan‑Act‑Reflect loop to run focused experiments, learn faster, and improve outcomes