Results for “multi-agent”

agenttrail: live map of a multi-agent build in the browser: which plan component is being worked on, by which agent or harness, what is done and what is stuck. Use when a multi-agent pipeline starts (/startcycle, /startcycle-graph, /teamwork-preview) or after a plan-canvas approve, when the user asks to see what the agents are doing ("live map", "build board", "who is running now"), or by yourself whenever a multi-agent run is under way — no user prompt needed.

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Agent-to-Agent (A2A) protocol implementation patterns for Google ADK - exposing agents via A2A, consuming external agents, multi-agent communication, and protocol configuration. Use when building multi-agent systems, implementing A2A protocol, exposing agents as services, consuming remote agents, configuring agent cards, or when user mentions A2A, agent-to-agent, multi-agent collaboration, remote agents, or agent orchestration.

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Break an RFC, a written design proposal, into work units with dependencies and acceptance tests for multiple agents. Organize research, implementation, testing, review, queued merges, and final system verification for features too large for one pass.

• Splitting a large design proposal into verifiable work units
• Assigning dependent tasks to multiple agents
• Managing review and queued merges of agent-built branches

Coordinate parallel AI agent sessions with dmux, a tool that manages terminal panes. Split development, research, testing, or review into separate tasks, isolate overlapping file changes, and review results before merging them.

• Running research and implementation in parallel
• Splitting database, API, and UI work across agents
• Coordinating test monitoring and fixes

Organize software development from requirements and architecture through testing, review, and deployment. Use specialized agent roles, shared memory, and test-driven development, where tests are written before code, to coordinate work across the project.

• Plan and implement a new feature
• Refactor legacy code with supporting tests
• Review code security and performance

Connect agent tasks into a sequence where each step receives the previous output. Use custom prompts or predefined workflows to analyze code, plan refactoring, generate tests, optimize performance, or transform data through multiple stages.

• Turn code analysis into an improvement plan
• Prioritize and refactor existing code
• Find test gaps and generate unit tests

Saves multi-agent workflows as named JSON files and runs them again without copying their definitions. Handles placeholders and run keys so repeated workflows can create fresh runs or deliberately reuse an existing run.

• Saving a workflow for repeated audits
• Running a saved workflow by name
• Sharing workflow definitions within a project

Design multi-agent workflows for research, reviews, and migrations. Organize work across different evidence sources, add independent checks, and combine findings while preserving source references, unresolved questions, and limits on what was examined.

• Plan research across multiple evidence sources
• Review implementation and tests from different angles
• Compare candidate solutions with independent judges

Plan and review systems where several AI agents work together. Assess whether multiple agents are useful, choose roles and communication methods, and define shared files, verification, and resource limits to address conflicts, spreading errors, and uncontrolled execution.

• Deciding between one agent and several agents
• Designing agent roles and communication
• Reviewing shared files and context boundaries

Improve workflows involving multiple AI agents by measuring bottlenecks, adjusting workload distribution, and managing model costs. This design guide helps compare changes against a measured baseline while checking quality, latency, and resource use.

• Finding bottlenecks in workflows involving multiple agents
• Reviewing task distribution and parallel execution
• Reducing model costs within quality thresholds

Design collaborative AI teams with CrewAI by defining each agent’s role, goals, tasks, and expected results. Covers passing work between agents, manager-led coordination, memory settings, and execution planning for workflows such as research and writing.

• Build a team that researches and writes articles
• Coordinate specialists through a manager agent
• Define agent roles and expected task outputs

Manage parallel feature development with Git worktrees, separate working folders for different branches. Assign distinct ports, copy environment files, optionally install dependencies, and check for uncommitted changes and merged branches before removing outdated worktrees.

• Develop several feature branches at the same time
• Prepare a separate workspace for an urgent hotfix
• Give each AI agent its own branch and working folder

Design security controls for Google Cloud Agent Gateway, covering incoming client requests and outgoing agent tool calls. Produce architecture diagrams, configuration files, deployment commands, and validation scripts using Model Armor, IAP, and Agent Registry.

• Design incoming and outgoing agent communication controls
• Prepare Model Armor filters and tool access policies
• Generate scripts to check authentication and allowed tool calls

Write and review launch prompts for agents working autonomously for hours or days. Define successful deliverables, excluded partial results, stopping rules, worker handoffs, and evidence-based review requirements so completion can be assessed against explicit criteria.

• Turning vague requests into precise task briefs
• Defining stopping rules for long autonomous runs
• Specifying deliverables for parallel workers

Plan and improve AI agent systems by managing the information models receive. This collection covers memory, agent coordination, tool design, context compression, evaluation, and the operating controls needed for reliable autonomous workflows.

• Planning information management for a new AI agent
• Investigating failures in long agent conversations
• Designing agent memory and multi-agent coordination

Plan projects that use large language models, from checking whether the task suits a model to designing processing stages. Estimate costs, choose single or multiple agents, and organize prototypes, structured outputs, and iteration.

• Checking whether a task is suitable for an LLM
• Designing a pipeline for batch analysis
• Estimating processing costs before development

Build and debug Python agents with Swarms, a framework for coordinating multiple AI agents. Covers tool connections, saved memory, autonomous execution, and workflow choices such as sequential steps, parallel work, specialist delegation, and combined answers.

• Building an agent that calls Python tools
• Coordinating sequential or parallel agent workflows
• Saving agent memory across restarts

Build servers and clients for A2A, an open protocol that lets AI agents communicate across frameworks. Publish agent capabilities, exchange tasks and results, stream progress, and add notifications for workflows involving multiple agents.

• Connect agents built with different frameworks
• Publish agent capabilities through an Agent Card
• Build a research, writing, and review workflow

Expert Java developer skill for AgentScope Java framework - a reactive, message-driven multi-agent system built on Project Reactor. Use when working with reactive programming, LLM integration, agent orchestration, multi-agent systems, or when the user mentions AgentScope, ReActAgent, Mono/Flux, Project Reactor, or Java agent development. Specializes in non-blocking code, tool integration, hooks, pipelines, and production-ready agent applications.

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Multi-Agent Orchestration - 蚁群仿生设计，定义Agent角色、生命周期、信息素通信、任务分解、冲突解决。当需要多Agent并行协作时路由到此。

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This skill should be used when the user asks to "create a team", "spawn agents", "assign tasks", "coordinate multiple agents", "check team status", "view kanban board", "send messages between agents", "manage team tasks", "monitor team progress", or mentions "clawteam", legacy "oh", "multi-agent coordination", "team collaboration", "agent inbox", "task board", "spawn worker". This skill should also be triggered when the current task is complex enough to benefit from splitting into subtasks and delegating to multiple agents — for example when the user asks to "build a full-stack app", "refactor the entire codebase", "implement multiple features in parallel", or when the agent determines that the work scope exceeds what a single agent can efficiently handle alone. Provides comprehensive guidance for using the ClawTeam CLI to orchestrate multi-agent teams with task management, messaging, monitoring, runtime profiles, git context, and recovery tooling.

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Generate a complete academic literature survey from scratch using multi-agent orchestration. Searches academic databases (OpenAlex, CrossRef, Unpaywall), downloads PDFs, builds BibTeX, drafts a LaTeX review, compiles it, reviews for quality, and revises based on feedback — all automated. Use this skill whenever the user asks to "generate a literature review", "write a survey paper", "review recent papers on [topic]", "create a survey of [field]", "综述", "文献综述", "survey papers in top journals", or wants a multi-agent pipeline to produce a publishable-quality literature review. Also trigger when the user wants to search multiple journals for papers on a topic and synthesize findings into a structured document. Works for any academic field but optimized for economics journals.

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Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory

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SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) comprehensive development methodology with multi-agent orchestration

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