The control plane for production AI agents
Version prompts, connect tools via MCP, run evaluations, and promote agents across environments — all in one operational workspace.
14-day free trial · Team workspaces · Multi-environment deployments
From configuration to production in three steps
Configure
Connect models, MCP servers, knowledge sources, and skills into versioned agent configurations.
Evaluate
Review captured conversations, inspect traces, run automated tests, and validate prompt changes before release.
Deploy
Promote validated versions across dev, staging, and production with full dependency visibility.
Everything you need to ship agentic systems
Four connected capabilities that take teams from first agent to governed production operations.
Build
Design agent capabilities with the same rigor you apply to application code.
- Version agents, prompts, and skills independently
- Connect MCP servers and map tools to skills
- Reuse capabilities across teams and environments
Validate
Understand what agents did in production, score quality, and catch regressions before customers do.
- Inspect real conversations with transcripts and runtime traces
- Annotate outcomes and run automatic conversation analysis
- Test prompts in the playground and run AI-native test suites
Operate
Run agents in production with clear release paths and operational visibility.
- Environment-based deployment promotion board
- Workflow and automation observability
- Centralized RAG knowledge management
Assist
Keep operational guidance and agent chat beside the work — not in a separate tool.
- Workspace-aware chat sidebar on every route
- Agent playground for runtime validation
- Compare versions and channels before release
Dive into every capability
Browse the full platform surface area by capability area.
One platform for all your AI agents
Manage the full lifecycle of every agent in your ecosystem, from creation and versioning to monitoring and multi-tenant deployment.
- Register and version agents across any language or framework
- Control visibility per environment for public and internal agents
- Filter by category and monitor deployment status
- Route between specialized agents with NLU-based handoffs
Scale from prototype to production without losing control
MASAIC is the operational layer for agentic systems — where design, tooling, evaluation, and release management stay connected as you scale from one agent to many.
Agent-to-Agent Protocol
Google's standard protocol for seamless communication between AI agents
Model Context Protocol
A universal protocol for connecting AI models with tools and data sources
Agent UI Protocol
A CopilotKit-based protocol for interactive AI chat experiences
From single agent to multi-agent systems
Start with a single specialized agent. As your needs grow, MASAIC lets you evolve to a hierarchical multi-agent topology with intelligent NLU-based routing without re-architecting your system.
Single-Agent System
One agent, many tools
One agent handles incoming queries and uses tools and MCP servers to accomplish different tasks autonomously.
Multi-Agent System
Agent Runtime plus specialized agents
MASAIC Agent Runtime selects the right agent, tools, and skills per request and orchestrates execution through explicit state transitions for more observable, deterministic operations.
Designed for production operations
Built for teams that need governance, isolation, and traceability as agent systems scale.
Multi-tenant workspaces
Separate team workspaces with role-based access and tenant isolation.
Multi-environment releases
Promote agent versions across isolated environments with dependency tracking.
Operational observability
Audit prompt changes, workflow runs, and deployment history from one workspace.
Open protocol support
Connect tools and agents through MCP, A2A, and AG-UI standards.
Ship your first agent with confidence
Create your workspace, connect your first MCP server, and promote an agent version in minutes.