agents▌

Build voice AI agents on LiveKit Cloud with structured workflows, minimal latency, and mandatory test coverage. \n \n Use LiveKit Cloud and LiveKit Inference for managed infrastructure and AI models without separate API keys \n Design agents around handoffs (agent-to-agent transitions) and tasks (scoped operations) to isolate context and reduce latency \n Every agent implementation requires tests covering basic conversation flow, tool invocation, error handling, and edge cases before deployment

Deploy and manage MCP-based declarative agents across Microsoft 365 with admin center governance, role-based access, and organizational distribution. \n \n Supports five agent types: organization-published, creator-shared, Microsoft-native, external partner, and frontier agents, each with distinct deployment and approval workflows \n Provides admin controls for publishing, deploying to user groups, blocking, and removing agents; requires AI Admin role for full management \n Includes MCP-specific

You are an agent architect who has learned the hard lessons of autonomous AI. You've seen the gap between impressive demos and production disasters. You know that a 95% success rate per step means only 60% by step 10.

Coordinate multiple specialized agents for complex tasks requiring diverse expertise domains. \n \n Includes 17 pre-built agents covering security, backend, frontend, testing, DevOps, database, mobile, API design, debugging, documentation, performance, planning, SEO, and game development \n Supports three orchestration patterns: comprehensive analysis (discovery through synthesis), feature review (domain-specific agents plus testing), and security audits (auditor plus penetration tester) \n Agen

Design and build autonomous AI agents with controlled autonomy, tool integration, and multi-agent orchestration. \n \n Covers six core capabilities: agent architecture design, tool and function calling, memory systems, planning strategies, multi-agent orchestration, and evaluation/debugging \n Provides three execution patterns: ReAct loops for step-by-step reasoning, Plan-and-Execute for task decomposition, and dynamic Tool Registry for managing available functions \n Identifies critical sharp e

AI agents that perceive screens, reason about actions, and control computers like humans do. \n \n Implements the perception-reasoning-action loop: capture screenshot, analyze with vision-language model, execute mouse/keyboard operations, repeat \n Covers Anthropic's Computer Use (Claude 3.5 Sonnet and Opus 4.5), with tool support for screenshots, mouse/keyboard control, bash execution, and file editing \n Requires sandboxed environments (Docker containers with virtual desktops) to isolate agent

Create and maintain minimal, high-signal agent documentation under 60 lines. \n \n Enforces research-backed best practices for agent-facing docs; instruction quality degrades with length \n Requires three core sections: Package Manager, File-Scoped Commands (per-file test/lint/typecheck), and Commit Attribution with agent identity \n Analyzes project structure (lock files, linter configs, CI commands, monorepo indicators) to determine what belongs in the file \n Uses headers and bullets only; re

Persistent, stateful AI agents with thread management, tool integration, streaming, and RAG on Convex. \n \n Thread management for multi-turn conversations with automatic persistence across restarts and real-time streaming responses to clients \n Tool integration allowing agents to execute Convex functions as callable tools for knowledge search, task creation, and external API calls \n Built-in vector search and RAG patterns for embedding documents and retrieving relevant context to augment agen

Foundation framework for building multi-step agents with built-in planning, memory, and skill delegation. \n \n Provides six core middleware options: task planning, filesystem context management, subagent delegation, persistent memory, human approval workflows, and on-demand skill loading \n Includes three always-present built-in tools: write_todos for task tracking, filesystem operations ( ls , read_file , write_file , edit_file , glob , grep ), and task for spawning specialized subagents \n Su

Guide for creating AGENTS.md files for project-specific inline rules.