SandboxAQ Launches Switch to Coordinate Multi-Framework AI Agents in Slack, Teams, and Discord

SandboxAQ has launched Switch, a framework-agnostic coordination layer designed to connect AI agents into existing enterprise chat environments, including Slack, Microsoft Teams, and Discord. The software is publicly available at no cost for self-hosted deployment on internal infrastructure. Switch addresses the operational fragmentation caused by disparate agent development frameworks. Rather than isolating autonomous assistants within bespoke web interfaces or terminal windows, the platform e

2 min
SandboxAQ Launches Switch to Coordinate Multi-Framework AI Agents in Slack, Teams, and Discord

SandboxAQ has launched Switch, a framework-agnostic coordination layer designed to connect AI agents into existing enterprise chat environments, including Slack, Microsoft Teams, and Discord. The software is publicly available at no cost for self-hosted deployment on internal infrastructure.

Switch addresses the operational fragmentation caused by disparate agent development frameworks. Rather than isolating autonomous assistants within bespoke web interfaces or terminal windows, the platform embeds agents directly into everyday human communication channels.

Shared Rooms and Multi-Framework Interoperability

Switch transforms standard chat channels into shared operational workspaces called "rooms." Inside a room, human operators and AI agents share unified conversation history, tool definitions, and contextual memory.

Key technical specifications include:

  • Framework Agnosticism: Switch supports agents built on diverse developer frameworks, including Claude Code, Google's Agent Development Kit (ADK), LangChain, and native OpenAI API toolchains.
  • Model Context Protocol (MCP) Integration: Connections to tools and underlying models leverage standard APIs and the Model Context Protocol, enabling teams to swap base LLMs or external integrations without refactoring workflows.
  • Persistent Context Across Transitions: State and session memory persist as participants join, leave, or hand off intermediate tasks between sub-agents.
  • Multi-Room Routing: A single deployed agent can maintain concurrent presence across multiple distinct rooms, applying separate permissions and context boundaries per channel.
SandboxAQ Switch Multi-Agent Coordination Architecture

Flint AI Ecosystem and Deployment Tiers

Switch represents the second component of SandboxAQ's Flint AI agent tooling suite:

  1. Flint CLI: A free, local command-line scanner used to evaluate agent performance, safety boundaries, and dependencies on developer workstations prior to deployment.
  2. Switch Console and Server: A desktop deployment console paired with a self-hosted server architecture that runs entirely on customer-managed hardware or private cloud instances.
  3. Flint Platform (Upcoming): A planned commercial cloud layer offering automated codebase agent discovery, runtime execution telemetry, and centralized guardrail governance.

Source Availability and Licensing Constraints

The Switch codebase is distributed under the Apache 2.0 license with the Commons Clause condition attached. Under this structure, enterprises may freely download, modify, run internally, and redistribute the software, as well as build private tools on top of it. However, third parties are restricted from packaging and selling Switch as a competing commercial managed service.

The release reflects a growing industry focus on orchestration middleware and agent discovery protocols as enterprises move from prototype evaluation to multi-agent production deployments.

Sources

Written by

More to read

  • Multi-Agent Orchestration Frameworks in Production: Comparing LangGraph, AutoGen, CrewAI, and LlamaIndex Workflows

    Production AI agent architectures have evolved past single-prompt loops and linear chains into complex multi-agent systems. When systems require multiple specialized models, tools, and validation gates to collaborate, selecting an orchestration framework determines the application's runtime latency, fault tolerance, state persistence, and debugging overhead. Four major frameworks dominate modern production multi-agent design: LangGraph, Microsoft AutoGen, CrewAI, and LlamaIndex Workflows. Each

    1 min
  • Group Relative Policy Optimization (GRPO): Mathematical Foundations, Group Baseline Advantage, Critic-Free Policy Gradients, and Reasoning Scaling

    Reinforcement learning from human feedback (RLHF) and reinforcement learning with verifiable rewards (RLVR) have become central to post-training large language models. For years, the default policy optimization algorithm in LLM alignment was Proximal Policy Optimization (PPO). While PPO offers stable policy updates through clipped surrogate objectives and Generalized Advantage Estimation (GAE), it introduces severe computational and architectural overhead when scaled to hundred-billion-parameter

    1 min
  • AWS and NVIDIA Expand AI Partnership to Deploy 2 Million Additional Blackwell Ultra and Rubin GPUs

    Amazon Web Services (AWS) and NVIDIA have announced a major expansion of their cloud infrastructure partnership, committing to deploy two million additional high-end NVIDIA GPUs across AWS global data centers in 2027 and 2028. The deployment expands on AWS's previous commitment from GTC 2026 to add one million GPUs starting in 2026, bringing total forward allocations across the multi-year cycle to three million units. The upcoming capacity will comprise NVIDIA Blackwell Ultra, Rubin, and Rubin

    1 min