02Evaluate
Evaluate Agents
Test against real-world scenarios. Catch regressions before users do. Pre-deploy gate that earns the deploy button.
Build agents
Build an agent. Or a team of them.
Visual builder, chat-to-create, multi-agent orchestration. Templates to start, custom tools and knowledge to go further, human-in-the-loop where it matters - without writing infrastructure.
Trusted by 150,000+ builders shipping production agents.
- 30+
- LLMs supported
- 500+
- MCP integrations
- 50+
- Agent templates
- 200+
- Built-in tools
Start fast
Start from a template - or a blank canvas.
Most people don't start from scratch. We don't ask you to. Pick from 50+ pre-configured templates and customize from there. Or open a blank canvas and build exactly what you need - top to bottom.
Customer Support
Refund handling, order lookup, FAQ resolution
Sales Qualifier
Lead scoring, discovery questions, CRM handoff
Document Intake
Extract, validate, route invoices and contracts
Research Analyst
Multi-source research with citations
Client Onboarding
Data collection, document verification, system provisioning
Internal Q&A
Company knowledge over Slack or Teams
Orchestration
A single agent answers questions. A team of agents runs a process.
One agent can hold a conversation. One agent cannot verify the order, check inventory, draft the refund email, update the CRM, and escalate the edge cases. That's why production agents on AgentX are built as teams - an orchestrator that coordinates specialized sub-agents, each with its own role, tools, knowledge, and permissions.
Clear role definition
Each sub-agent has a defined scope. Prevents topic drift and context explosion when conversations span domains the agent wasn't designed for.
Agent-level access control
Each sub-agent sees only what it needs. The orchestrator can call sub-agents the end user can't directly invoke. Reverse-engineering one sub-agent doesn't compromise the others.
Goal-driven execution
Multi-round reasoning. Sub-agents escalate to the orchestrator, hand off context to peers, or pause for human input. Not a linear chain - a team.
Agent capabilities
Give your agents what they need to actually work.
An agent is the LLM plus everything you give it access to. The capability surface is what separates a chatbot from something that gets the job done.
01
Skills
Pre-built behaviors and abilities. Lead generation, content extraction, image understanding, document analysis, structured output. Toggle them on per agent. Stack them when one isn't enough.
02
Tools
200+ built-in (PDF, Excel, Slides generators, image and video gen, scheduling, lead capture). 1,000+ via MCP. Custom Python tools via the in-platform editor. The agent calls them - you don't wire them up.
03
Knowledge
Private RAG database per agent or team. Hybrid search with reranking. High-fidelity parsing for complex PDFs and large spreadsheets. Citations back to the source on every answer.
04
Memory & context
Persistent conversation memory, customizable context windows, onboarding facts the agent always remembers. Different memory rules per sub-agent in a team.
Deterministic control
Free-form where it should be. Deterministic where it has to be.
Pure agent reasoning is great for open-ended work. It's the wrong tool for “if invoice total > $10K, escalate to a manager and pause.” For deterministic control, we have a visual workflow editor. For human oversight, you can drop a human-in-the-loop checkpoint anywhere in the flow.
- Chain multiple agents into a deterministic pipeline with branching logic
- Drop human-in-the-loop checkpoints - pause for approval, route to a queue, escalate before continuing
- Trigger on webhook, schedule, email, or API call
Bring your own model
Use GPT. Or Claude. Or Gemini. Or whatever ships next quarter.
Models change every few months. Your agent shouldn't be rewritten when they do. AgentX is model-agnostic at every layer - orchestrator, sub-agents, evaluation, embeddings. Use different models for different roles inside the same agent team. Switch models without touching your workflow.
- 30+ models supported, updated as new ones ship
- Custom model endpoints - Azure OpenAI, AWS Bedrock, self-hosted, private deployments
- Different models per sub-agent - cheap models for routing, premium for reasoning
- Cost and latency tradeoffs visible at the agent level before you deploy
Built for real users
Production controls from day one, not after the security review.
The workspace you build in is the workspace agents run in. RBAC, audit logs, and isolation aren't a separate tier - they're how the platform works. So when your agent goes from prototype to real users, you don't migrate. You deploy.
RBAC
Granular permissions for admins, editors, viewers across every workspace.
Workspace isolation
Separate data, agents, and knowledge bases across departments, clients, or tenants.
Sub-agent injection resistance
Sub-agents can't be reverse-engineered through the orchestrator. Prompt injection containment built into the architecture.
Full audit logs
Every agent action, tool call, knowledge retrieval, and human override logged. SSO via SAML / OIDC. Cloud or on-prem.
Build → Evaluate → Deploy
Build is step 1. Evaluate is step 2.
A built agent is a prototype. A built agent that passes evaluation against your real test cases is something you can put in front of users. That's why the platform doesn't end at the build canvas.
03Deploy
Deploy Agents
One-click to API, Slack, Teams, WhatsApp, web, email, voice. Versioned. Rollback in a click. Logs and traces on every run.
Get started
Your first multi-agent team is 10 minutes away.
Free to start. No credit card. No onboarding call. Build, save, share - and deploy when you're ready.
Professional/Business
$199 - $299
/ month
Agencies and service teams with white-label deployment.
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