Should a Small Company Use a Local LLM or an API?
Use a workflow scorecard to choose a local LLM, an API, or a hybrid pilot based on data boundaries, workload shape, quality, and ownership.
Topic collection
AI architecture
Choose between workflows, retrieval, models, tools, memory, and agents.
How to Design Graceful Degradation for AI Features
Design AI features that lose capability safely when models, data, or tools fail, with fallbacks, stop conditions, honest UX, and tests.
When Should I Use Structured Outputs vs Function Calling?
Choose structured outputs for typed model responses, function calling for executable capabilities, and both when a workflow crosses both boundaries.
How to Make an AI Agent Ask Clarifying Questions
Make an AI agent ask useful clarifying questions by defining a typed pause, blocking tools until the answer arrives, and testing when to proceed.
What Should an AI Agent Design Document Include?
Build an AI agent design document around the job, boundaries, behavior, evidence, ownership, and release conditions before implementation.
How to Secure an MCP Server for AI Agents
A practical MCP server security guide covering OAuth audience checks, tool scope, sandboxing, prompt injection, SSRF, supply chain, and audit controls.
How to Design an AI Agent State Machine
Design an AI agent state machine with explicit state, guarded transitions, safe side effects, persistence, recovery paths, and tests you can run before production.
How to Prevent AI Agent Memory Poisoning
A practical control plan for stopping malicious or stale content from becoming persistent AI-agent memory and shaping later tasks.
Should I Use RAG or an AI Agent for Internal Knowledge?
Choose RAG for grounded answers, a fixed workflow for known steps, and an agent only when evidence must change the next search or action.
How to Design Multi-Agent Handoffs That Preserve Context
Design multi-agent handoffs as bounded contracts for context, artifacts, authority, verification, and failure instead of passing loose transcripts between prompts.
How to Design an AI Agent Memory Schema
A vendor-neutral schema for scoped AI-agent memory records, with promotion, retrieval, validation, expiry, and deletion rules.
When Should I Use a Single AI Agent Instead of Multiple Agents?
Start with one AI agent when one coherent context can solve the task. Split only for real parallel work, hard permission boundaries, or measured limits.
What Should an AI Agent Remember Between Tasks?
A practical boundary for deciding what an AI agent should remember, recompute, reference, or forget between tasks.
When Should You Use an AI Agent? A Decision Framework
Use five hard gates to decide whether a workflow needs an AI agent, a fixed LLM workflow, or ordinary automation before you spend money or grant access.