When Should an AI Workflow Own a Decision Versus Prepare One?
A 32-case comparison shows when an AI workflow should prepare evidence, recommend an action, require approval, or execute within policy.
Topic collection
AI architecture
Choose between workflows, retrieval, models, tools, memory, and agents.
Do Simpler AI Architectures Outperform Clever Ones on Changing Workflows
A 12-fixture change-set benchmark compares fixed workflows, single agents, and orchestration on regression, repair effort, latency, cost, and traces.
What AI Architecture Decisions Should Teams Explain to Non-Specialists?
Use this six-decision matrix to explain an AI workflow's boundaries, trade-offs, owners, consequences, and verification evidence.
How to Design an AI Workflow Around Reversible Actions
A provider-neutral action contract and failure matrix for deciding what an AI workflow may run, stage, approve, compensate, or block.
What Architecture Skills Make Professionals Effective With AI Systems?
Architecture skill means defining the job, choosing workflow or agent, bounding authority, and testing the result with evidence.
Should an AI Integration Use MCP or a Custom API?
Use a custom API for one embedded client. Extract MCP when independent model clients or a reusable platform create real reuse, with security vetoes.
Should I Use RAG or SQL for Internal Business Questions?
A reproducible route benchmark on synthetic business questions shows where SQL, RAG, and a hybrid router each answer, cite, or abstain.
How Many LLM Calls Should an AI Workflow Use?
A six-task deterministic audit shows when one LLM call is enough, when a second owns a real contract, and what a model run must measure.
When Should an AI Workflow Use Deterministic Rules Before an LLM?
A worked matrix for deciding which AI workflow checks belong in deterministic code, model judgment, or a human decision gate.
When Should an AI Workflow Run Synchronously or Asynchronously?
A matched 40-job experiment shows when sync is cheaper and when async earns its queue, status, retry, and recovery complexity.
How Much Latency Does an LLM Gateway Add?
A bounded audit of seven gateway-latency claims explains what direct comparisons can prove, what mock tests hide, and how to run the next test.
Do AI Agents Get Worse With More Tools?
A five-task deterministic audit shows when orthogonal tools stay harmless, when overlapping schemas create ambiguity, and how to test a real agent honestly.
How to Choose: Model Instructions, Application Logic, or Policy Service
A six-control matrix for deciding what belongs in model instructions, deterministic application logic, or a shared policy service.
How Much Decision Context Should Human Reviewers See?
A bounded fixture compares how much decision context human reviewers need across final-only, decision-summary, and expandable-trace surfaces.
How Should an AI Workflow Handle Database and Document Conflicts?
A reproducible eight-case fixture shows how to preserve source authority, expose conflicts, and abstain when mixed-source evidence cannot establish current state.
How to Test Automatic Routing Across AI Tasks
A fixed ten-case replay shows how to test automatic routing against explicit rules and one capable path before adding another architecture layer.
When Should an AI Workflow Use a Database Query Before a Model Call?
A trace-backed routing matrix for choosing query-first, model-first, or fixed-route AI workflows when structured data is involved.
Should an Internal AI Assistant Use Retrieval, Tools, or Both?
Use retrieval for approved knowledge, tools for live state, and both when one answer must join policy evidence to a current system result.
How to Test a Versioned Policy Update in an AI Workflow
A 20-case harness compares embedded policy logic with a versioned policy layer across thresholds, exceptions, permissions, and approvals.
Which Architecture Keeps a Model From Becoming the System of Record?
Use a workflow runtime, not the model, to own facts, writes, retries, approvals, and recovery. This matrix and fixture make the boundary testable.
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.
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.