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Articles filed under AI Architecture in the Journal.
22 articles
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Better AI coding models expose the coordination layer your team never assigned: active task state, blockers, approvals, artifacts, and handoffs that survive Cursor, Claude Code, Codex, and closed sessions.
Your team does not need another model ranking. It needs to decide where control lives, who owns shared state, and when MCP-connected project state becomes the missing layer.
Spec-driven development tools are not converging on one winner. The useful decision is where durable project memory lives after the AI session ends: in specs, in git, or in an external MCP-connected board.
An MCP badge does not prove an integration is agent-ready. Use this audit to separate read-only wrappers from servers that let assistants update durable, team-visible project state.
A Claude Code project board is a shared MCP-readable system of record for scope, decisions, tasks, artifacts, and workflow locks. Here is when local memory is enough, when it stops scaling, and how Agiflow keeps state durable across sessions.
AI agent secrets management starts by shrinking what the agent host can see, then moving raw values into runtime delivery or workload identity when the task needs stronger boundaries.
MCP project management tools are forming around durable project state for external AI assistants. Here is the launch evidence, safety checklist, and where Agiflow fits.
AI coding agents lose context when the prompt becomes both working memory and system of record. The durable fix is task state: scope, criteria, tests, status, artifacts, and handoff notes.
A practical guide to Claude Code and Codex orchestration, with role boundaries, evidence-based handoff contracts, MCP project state, and verification gates.
Token efficiency is a tool-architecture problem, not just prompt hygiene. Use Agiflow benchmark data, MCP guidance, and production measurement rules to reduce AI coding token use without hiding reliability costs.
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