Why Does AI Fail When Integration Choice Is Unclear?
A paired direct-function and MCP test shows how to classify integration failures and assign repair ownership after a promising demo.
Topic collection
AI architecture
Choose between workflows, retrieval, models, tools, memory, and agents.
What Evidence Should Technical Buyers Collect About MCP or API?
A reproducible MCP-versus-API test packet for permissions, traces, failure recovery, rollback, cost fields, ownership, and veto decisions.
Why Does AI Fail When Steps Drift? A Trace-Based Test
A 60-run fixture shows why rolling context misses drift and where typed step contracts catch it before silent downstream corruption.
How Can a Product Engineer Practice Owning an AI Workflow?
Use a dated two-track fixture to test whether a product engineer can run, explain, correct, and safely hand off an AI workflow without the author's context.
Why Does AI Fail When Systems Disagree?
A three-case fixture shows why AI cannot safely choose between conflicting CRM, billing, and support records without authority and abstention rules.
How Should a Team Rank Renewal-Risk Review Before an AI Pilot?
A worked scorecard ranks manual review, human-approved AI assistance, and autonomous renewal-risk actioning before a pilot.
How Should a Team Decide Customer Onboarding Exceptions?
Use a 12-case evidence matrix to choose continue, rework, alternate path, approval, or stop for customer onboarding exceptions.
What Does the Evidence Show About Policy-Changing Workflows in Practice?
A reproducible 18-record workflow test shows what advisory, enforced, and exception-bearing policy revisions change in reachable actions and evidence.
How to Choose an Architecture for Changing Approval Policies
Choose application code, a policy decision point, or a workflow by testing policy changes, stale approvals, ownership, and rollback before you migrate.
How to Practice State Ownership Before an AI Pilot
Use a worked owner and writer matrix plus five replay cases to expose state failures before an AI pilot receives real permissions.
How to Build State Ownership When Policies Change
A runnable state-ownership test for policy versioning, replay, projections, compensation, duplicate delivery, and conflicting writes.
How to Verify an AI Recovery Handler Before Adding Control Flow
A 28-case benchmark shows when exception branches create path and test debt, when a typed handler wins, and when recovery should escalate.
Design Architecture for Tenant-Specific Workflows: Practice Guide
A hands-on practice guide for choosing shared or isolated tenant workflows, carrying tenant context, scoping state, and testing action boundaries.
How to Decide Confidence Routing After a Failed Prototype
A worked routing test shows when confidence thresholds justify review, redesign, or stopping after an AI prototype fails.
How to Measure Batch Review With One Internal Owner
A reproducible one-owner experiment shows how to compare batch sizes by intervention catch rate, decision time, expiry, and attention cost.
When Should an AI Workflow Stop Instead of Guessing?
A 35-case fixture shows when an AI workflow may continue and when it must retry, compensate, quarantine, escalate, or stop.
How to Set an AI Autonomy Boundary Before Production
Set an AI autonomy boundary with a reliability-first scorecard before choosing a workflow, single agent, or multi-agent system for production.
When Should an AI Workflow Split Into Subworkflows?
A deterministic fault-injection test shows the first exception branch that breaks causal reconstruction, plus a trace rubric for deciding what to split.
What Architecture Evidence Should Be Required Before an AI Pilot?
Require a bounded workflow, trusted data path, safe action boundary, replayable traces, evaluation baseline, and named ownership before an AI pilot.
When Is an AI Workflow Problem Actually a Data Problem?
A five-case deterministic fixture shows when verified evidence fixes an AI workflow failure, and when the real fault is the contract, permission, or evaluator.
Why AI Architecture Diagrams Hide the Human Operating Model
A component diagram can show an AI workflow and still hide who decides, approves, operates, evaluates, and repairs it. Use this before/after test.
What Should an AI Agent Run Contract Contain?
An AI agent run contract names its goal, inputs, state, tools, permissions, stop rules, evidence of progress, escalation path, owner, and retained trace.
How to Draw an AI Workflow Boundary Around Systems of Record
A worked boundary diagram, ownership worksheet, and seven contract tests for keeping AI workflows outside systems of record.
Why AI Prototypes Fail When the Workflow Has Multiple Owners
A reproducible four-case fixture shows why an AI prototype can pass its task while ownership, authority, or source-of-truth conflicts stop the workflow.
AI Workflow Interaction Contract Worksheet
A filled worksheet for turning an AI feature's user interaction, approval, recovery, and concurrency needs into a request and status contract.
How to Map Decision Boundaries Before Designing an AI Workflow
A worked decision-boundary map and fail-closed validator for deciding what an AI workflow may observe, propose, route, or execute.
How to Choose AI Architecture When the Workflow Changes by Team
A worked matrix for choosing RAG, workflow, single-agent, or multi-agent architecture when ownership, approval, and execution paths change.
When Should an AI Workflow Split Extraction From Decision Support?
A reproducible six-record test shows when typed extraction and policy-bound decisions justify an extra workflow stage.
Why AI Architecture Fails When the Source of Truth Changes
A runnable fixture shows how an ownership switch turns stale retrieval into wrong AI answers, citations, and tool decisions.
When Should an AI Workflow Have a Separate Policy Layer?
A worked decision matrix and bypass test show when embedded checks stop being enough, and what the smallest passing policy architecture looks like.
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.
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.