「multi-agent」の検索結果

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

日本語の概要は準備中です。原文の説明を表示しています。

AI Agent Architect: Design agent systems: single vs multi-agent, tools and MCP, memory, guardrails, evals.

日本語の概要は準備中です。原文の説明を表示しています。

Guides architectural decisions for LangGraph applications. Use when deciding between LangGraph vs alternatives, choosing state management strategies, designing multi-agent systems, or selecting persistence and streaming approaches.

日本語の概要は準備中です。原文の説明を表示しています。

Implements stateful agent graphs using LangGraph. Use when building graphs, adding nodes/edges, defining state schemas, implementing checkpointing, handling interrupts, or creating multi-agent systems with LangGraph.

日本語の概要は準備中です。原文の説明を表示しています。

Autonomous novel writing CLI agent with web workbench (JiaOS Studio) - use for creative fiction writing, standalone short-fiction packages, cover generation, novel generation, style imitation, chapter continuation/import, EPUB export, AIGC detection, and fan fiction. Native English support with 10 built-in English genre profiles (LitRPG, Progression Fantasy, Isekai, Cultivation, System Apocalypse, Dungeon Core, Romantasy, Sci-Fi, Tower Climber, Cozy Fantasy). Also supports Chinese web novel genres (xuanhuan, xianxia, urban, horror, other). Multi-agent pipeline, two-phase writer (creative + settlement), stronger long-form chapter craft rules, hook-ledger payoff checks, 33-dimension auditing, token usage analytics, creative brief input, structured logging (JSON Lines), multi-model routing, custom OpenAI-compatible provider support, and JiaOS Studio web UI for visual book management, short-fiction runs, cover generation, chapter review, real-time writing progress, market radar, and analytics.

日本語の概要は準備中です。原文の説明を表示しています。

Expert guide for speculative multi-agent drafting, parallel hypothesis generation, AST-aware consensus arbitration, and multi-model code reconciliation / Panduan ahli penyusunan draf multi-agen spekulatif, generasi hipotesis paralel, arbitrase konsensus sadar-AST, dan rekonsiliasi kode multi-model.

日本語の概要は準備中です。原文の説明を表示しています。

Expert guide for AI-assisted coding workflows — agentic code generation, multi-agent code swarms, self-healing CI/CD, automated PR review, spec-to-code pipelines, codebase knowledge graphs, and human-in-the-loop approval gates / Panduan ahli untuk workflow pengkodean berbasis AI — generasi kode agentic, code swarm multi-agen, CI/CD self-healing, review PR otomatis, pipeline spec-to-code, knowledge graph codebase, dan gate persetujuan human-in-the-loop.

日本語の概要は準備中です。原文の説明を表示しています。

The philosophy and practical benefits of agent fungibility in multi-agent software development. Why homogeneous, interchangeable agents outperform specialized role-based systems at scale.

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MCP Agent Mail for multi-agent coordination. Use when agents need file locks, messaging, inboxes, or conflict prevention. Handles macro_start_session, file_reservation_paths, send_message, threading, pre-commit guards.

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Jeffrey Emanuel's multi-agent implementation workflow using NTM, Agent Mail, Beads, and BV. The execution phase that follows planning and bead creation. Includes exact prompts used.

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Generates publication-quality academic figures (framework diagrams, pipeline illustrations, system architectures, method overviews) from a paper's method text and a target caption, using a local PaperBanana multi-agent pipeline (Retriever → Planner → Stylist → Visualizer → Critic).

日本語の概要は準備中です。原文の説明を表示しています。

Northbridge coordination pattern for multi-agent orchestration. Creates convoys, dispatches work via sling, monitors progress, and handles escalations. Never executes work directly.

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Before dispatching a multi-agent team, run a spectral diagnostic on the proposed communication graph and emit a (ρ, Δ, κ) coordination signature plus a pass/fail verdict against per-task-class thresholds. Builds the row-stochastic operator P from the team graph, computes the successor representation M = (I − γP)⁻¹, and ranks the topology for robustness (κ, condition number), consensus (Δ, spectral gap), and drift (ρ, spectral radius). Per Parks & Alharthi (arxiv 2605.11453), rank order on (κ, Δ, ρ) predicts coordination quality pre-execution with rank correlations of 1.0 / 0.5 / −1.0. Triggers: "dispatch the team", "team topology check", "before running the agents", "is this team configuration OK", "team pre-flight".

日本語の概要は準備中です。原文の説明を表示しています。

Agreement protocol in multi-agent systems

日本語の概要は準備中です。原文の説明を表示しています。

Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination

日本語の概要は準備中です。原文の説明を表示しています。

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.

日本語の概要は準備中です。原文の説明を表示しています。

Use when implementing a Python feature, adding CLI commands, writing pytest suites, reviewing Python code, debugging, or refactoring. The primary Python engineering workflow orchestrator — classifies the task and delegates through this plugin's own specialist agents (architect → implement → test → review), sized to the task. Delegates to python-cli-architect (implementation), python-pytest-architect (tests), code-reviewer (review), python-cli-design-spec (architecture). Triggers on any Python task requiring specialist agent coordination or multi-agent execution.

日本語の概要は準備中です。原文の説明を表示しています。

Orchestrator context window discipline enforcement. Prevents the orchestrator from reading source files it will not edit, running diagnostic commands that waste context, and rationalizing delegation bypasses. Use when setting up orchestrator guardrails, reviewing delegation discipline, or diagnosing context window waste in multi-agent workflows. Activates PreToolUse hooks that surface decision points before source file reads and diagnostic command execution.

日本語の概要は準備中です。原文の説明を表示しています。

Multi-agent adversarial verification with convergence loop. Two independent review agents must both pass before output ships.

日本語の概要は準備中です。原文の説明を表示しています。

RFC-driven multi-agent DAG execution pattern with quality gates, merge queues, and work unit orchestration.

日本語の概要は準備中です。原文の説明を表示しています。

Patterns and architectures for autonomous Claude Code loops — from simple sequential pipelines to RFC-driven multi-agent DAG systems.

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Turn a one-line objective into a step-by-step construction plan for multi-session, multi-agent engineering projects. Each step has a self-contained context brief so a fresh agent can execute it cold. Includes adversarial review gate, dependency graph, parallel step detection, anti-pattern catalog, and plan mutation protocol. TRIGGER when: user requests a plan, blueprint, or roadmap for a complex multi-PR task, or describes work that needs multiple sessions. DO NOT TRIGGER when: task is completable in a single PR or fewer than 3 tool calls, or user says "just do it".

日本語の概要は準備中です。原文の説明を表示しています。

Comprehensive L&D framework for upskilling DevOps/IaC/Automation teams to become AI Agent Engineers. Covers LLM literacy, RAG, agent frameworks, multi-agent systems, and LLMOps. Designed to help traditional automation teams compete with OpenAI and Anthropic.

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Build production-ready AI agents with Microsoft Foundry and Agent Framework. Use when creating AI agents, selecting LLM models, implementing agent orchestration, adding tracing/observability, or evaluating agent quality. Covers agent architecture, model selection, multi-agent workflows, and production deployment.

日本語の概要は準備中です。原文の説明を表示しています。