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No-code platform for building and running AI agents that automate work across data, sales and support tasks.
Open-source testing platform where AI agents drive your app end-to-end to catch regressions on every PR, no test code needed.
AI coding platform and IDE that orchestrates multiple agent sessions and lets teams plug in their own AI subscriptions.
Enterprise AI agent platform for building, deploying, and governing no-code agents across company knowledge and workflows.
AI21 Maestro is an enterprise framework that optimizes cost, accuracy and latency of production AI agents.
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- ✦Visual canvas to orchestrate multi-agent workflows
- ✦Prebuilt specialized agents (data, support, CRM, sales)
- ✦Access to many AI models with no vendor lock-in
- ✦Slack, Teams and email agent interaction
- ✦Recurring/scheduled tasks and triggers
- ✦Enterprise security: RBAC, VPC, audit logs, spend controls
- ✦AI agents navigate your app in real browsers
- ✦One-command setup that maps flows and drafts tests
- ✦SDK to seed and tear down real data
- ✦Runs on every PR against preview deploys
- ✦No test code required
- ✦Open-source with a self-hosted option
- ✦Integrates with GitHub, Vercel and Linear
- ✦AI coding IDE with agent orchestration
- ✦Run and manage multiple agent sessions
- ✦Task, artifact and collaboration tools
- ✦Bring-your-own AI subscription or API keys
- ✦Cloud-scale agent execution
- ✦No-code visual agent builder
- ✦Company knowledge search across docs and wikis
- ✦Scheduled and recurring agent automations
- ✦Model-agnostic access to major AI models
- ✦Browser extension integration with 30,000+ apps
- ✦Data analysis of spreadsheets and reports
- ✦Enterprise SSO/SCIM/SAML and dedicated deployment
- ✦Agent cost optimization
- ✦In-flow result validation
- ✦Advanced RAG data extraction
- ✦Dynamic planning and orchestration
- ✦Cost attribution and budgeting
- →Automate data analysis and reporting
- →Triage support tickets and spot patterns
- →Keep a CRM updated and research prospects
- →Deploy AI agents across a team's tools
- →Catching regressions before merging PRs
- →End-to-end testing without writing scripts
- →QA for fast-shipping product teams
- →Self-hosted testing on your own infrastructure
- →Shipping code faster with AI agents
- →Coordinating agent work across a team
- →Managing tasks and artifacts in one place
- →Running many parallel agent sessions
- →Automating recurring reports and workflow tasks
- →Searching internal company knowledge with AI agents
- →Deploying AI copilots for sales, support, or legal teams
- →Running enterprise AI with strict data governance requirements
- →Optimizing production AI agents
- →Reducing agent compute spend
- →Improving RAG accuracy
- →Enterprise workflow automation