AgentX

AgentX is a web-based platform for building, evaluating, and deploying AI agents and multi-agent workflows. It targets both solo builders who want to ship production agents themselves and operations teams who want a managed automation service. The homepage states it is trusted by 150,000+ users worldwide.

What It Is

AgentX positions itself as an end-to-end AI agent platform organized around three pillars: Build, Evaluate, and Deploy. Unlike platforms that stop at prototyping, AgentX includes a structured evaluation framework — test datasets, LLM-as-a-judge scoring, and regression tracking — before any agent reaches production. It also offers a managed "We Build It For You" track where the AgentX team scopes, builds, deploys, and operates a workflow on behalf of enterprise customers.

How the Build–Evaluate–Deploy Loop Works

The platform's core workflow follows three sequential stages:

• Build: A drag-and-drop visual builder lets users design multi-agent workflows. An orchestrator agent coordinates specialized sub-agents, each with its own role, knowledge access, and permissions. Users can bring their own LLM or choose from supported models, add custom tools via code or chat, and configure human-in-the-loop checkpoints at any step.
• Evaluate: Before deployment, agents run against test datasets built from real historical cases. The system measures accuracy per field and scenario, surfaces edge cases and failure modes, and generates AI-powered analysis with suggested fixes. Post-deploy, continuous monitoring tracks drift and routes low-confidence outputs to human review.
• Deploy: One-click deployment to API, Slack, Microsoft Teams, WhatsApp, web widget, email, or voice. Deployments are versioned with instant rollback, and every run produces full logs and traces.

Multi-Agent Architecture and Knowledge Layer

AgentX is built for teams of agents rather than single chatbots. The orchestrator–sub-agent model gives each agent a defined role and scoped data access, which the product page says reduces context explosion and improves resistance to prompt injection. A dedicated Knowledge Layer provides each agent with a private RAG database, hybrid search with re-ranking, knowledge graphs for structured relationships, and high-fidelity parsing of complex PDFs, spreadsheets, and scans.

Enterprise Deployment and Security

The enterprise track supports cloud, hybrid, and full on-premise deployment. Security controls listed on the enterprise page include:

• Encryption in transit (TLS 1.3) and at rest (AES-256)
• Role-based access control (RBAC) with granular permissions
• SSO via SAML 2.0 / OIDC
• Workspace isolation across departments or tenants
• Sandboxed MCP tool execution
• Full audit trail on every agent action
• No customer data used to train external models

The enterprise page notes alignment with SR 11-7, DORA, EU AI Act, MAS, HKMA, GDPR, and SOX frameworks, and states a SOC 2 report is available on request. Average time from scope to production for the managed track is stated as under 30 days.

Integrations and Tools

AgentX connects to the stack teams already use. The product page lists built-in tools (image generation, document generation, web search), pre-configured MCP connections (Calendly, Google Sheets, Mailchimp, and others), and support for custom tools written in code or via conversational "vibe-coding." Deployment channels include Slack, Teams, WhatsApp, web widget, email, and voice. The platform also supports webhook-driven workflows and scheduled triggers.

Who It Is For

The platform serves three distinct audiences as described on the pricing page: solo builders and internal teams shipping their own agents; agencies and consultancies deploying white-labeled agents for clients; and operations leaders at companies with roughly 100–1,500 employees who want a manual process automated without managing a platform themselves. The white-label plans include dedicated client workspaces so end clients never see the AgentX brand.