AgentX

Most AI agent platforms sell you a builder and stop there. AgentX, from the team at agentx.so, organises itself around the parts that come after: evaluating whether the thing works, shipping it to a real channel, and watching it once it is live. The pitch is "Build → Evaluate → Deploy in a single workspace," and the product backs that with a visual multi-agent builder, a test-and-score evaluation layer, one-click deployment to ten channels, and full traces on every run. In my testing, it is a genuinely broad lifecycle platform with unusually transparent pricing — with one catch I will get to, which is that the deepest evaluation tooling is reserved for enterprise contracts.

What it actually does

Build. A visual canvas for multi-agent workflows: an orchestrator agent delegates to specialist sub-agents, each with its own role, knowledge access and permissions. Tools, memory, branching logic and human-in-the-loop checkpoints are all drag-and-drop, and there is a chat-to-build mode for creating and editing workflows conversationally. The vendor claims 52+ AI models (bring your own or use theirs) and 200+ integrations and MCPs, including pre-configured connections like Calendly, Google Sheets and Mailchimp. The Knowledge Layer gives each agent or team a private RAG database with hybrid search and re-ranking, knowledge graphs for structured relationships, and high-fidelity parsing for complex PDFs, spreadsheets and scans. Agent-level access control keeps each sub-agent's context scoped — the stated benefit is both relevance and resistance to prompt injection across agents.

Evaluate. Test datasets can be built from logs, CSVs or by hand; runs are versioned; scoring is LLM-as-judge with explicit reasoning and pass criteria you define, with "yellow" rows flagged for human review. Reports trace every action, tool call and data-layer access across multi-step, multi-agent runs, and each evaluation produces AI-generated analysis of what failed and what to change. The evaluation layer is framed as agent CI/CD: block a deployment when evals fail, promote when they pass, and keep evaluating after deploy.

Deploy. One click to API, Slack, Microsoft Teams, WhatsApp, web widget and email (voice is among the ten claimed channels), with versioned deployments, instant rollback, usage analytics, conversation logs, scheduled triggers and webhook-driven workflows. Enterprise adds RBAC, workspace isolation, sandboxed MCP execution, audit logs, SSO, dedicated infrastructure and on-premises deployment. There is also a framework-agnostic governance surface (documented at developers.agentx.so) that traces, scores and improves agents built in LangChain/LangGraph, CrewAI, the OpenAI Agents SDK, AutoGen, Google ADK, LlamaIndex, LiteLLM or plain provider SDKs — without touching the agent's own code or prompts.

Pricing and credits

Pricing is public and, at time of writing, reads as follows. Free: $0 forever — 1 workspace, up to 5 agents, 200 one-time credits, 1 seat, multi-agent workflows, API access (no production deployment). Solo Builder: $49/month ($490/year) — unlimited workspaces, 25 agents, 5,000 credits a month, extra credits at $10 per 1,000, production deployment. Professional: $199/month ($1,490/year) — 25 agents, 10,000 credits, 2 seats (extra seats $10/month each), white-label deployment, client workspaces. Business: $299/month ($2,990/year) — unlimited agents, 20,000 credits, priority support and SLA. Enterprise: custom, and the only tier with the full evaluation program, SSO, dedicated infrastructure and on-premises.

Credits are the universal unit: every response, extraction or tool call spends them at rates that depend on the model — 1 credit for fast/basic models (GPT-5 nano, Gemini Flash, Haiku-class), 2 for balanced, 3 for advanced (GPT-4o, Claude Sonnet-class), 4 for expert, 5 for the top reasoning models. Knowledge ingestion costs a one-time 1 credit per ~3,000 characters of text; after that, querying your own documents is free. Image generation runs 5-10 credits per image and video 15-40 credits per second depending on model. The vendor's own cost scenarios on the pricing page run from roughly $79/month for a personal agent to $349/month for a small agency workload.

My testing notes

First, the build step is fast. I had a two-agent workflow — an orchestrator routing to a research sub-agent with its own document knowledge — running without writing infrastructure code, and the credit meter matched the published rates: basic-model responses cost 1 credit each, and a 3,000-character document embed cost exactly the 1 credit the documentation says. Credit transparency is the strongest operational feature here; I always knew what a run would cost before starting it.

Second, the evaluation loop is real but tier-gated. On a self-serve plan you get what the pricing table calls "demo evaluation mode" — limited runs with sample datasets and basic metrics. That was enough to see the shape: I ran a small test set, got a score in seconds, and the generated failure analysis named a specific tool call to fix. But the full evaluation program — the CI/CD gating, the deeper regression tracking that the marketing leads with — is Enterprise-only. For a platform whose differentiation is "we don't stop at build," reserving the prove-it step for the most expensive tier is a strange inversion, and buyers should price that expectation explicitly.

Third, deployment behaved as advertised within the self-serve envelope: versioned rollout with instant rollback, logs and traces on every run, and scheduled triggers firing on time. The framework-agnostic tracing path (LangChain and the OpenAI Agents SDK in my checks) means teams with existing agents are not forced to rebuild them on AgentX's canvas to get observability.

Usability and support

The builder follows the mental model it advertises: you design how agents collaborate on a canvas rather than wiring them in code, and the chat-to-build mode is a genuine shortcut for scaffolding a workflow before you refine it in the visual editor. Templated prompts cover common use cases, and the pricing page's "your first agent is 10 minutes away" is marketing framing, but it is not far off — my two-agent prototype was answering questions over its own document knowledge within the first sitting. Documentation splits between marketing-grade product pages and a thinner developer portal; the credit guide is the most useful document on the site. Support on Business gets priority handling and an SLA; everything below is standard channel support, and the free tier is explicitly self-serve. The MCP server hub the team maintains is a small but practical ecosystem play — verified protocol servers mean fewer integration projects on day one.

What it does not do

Full evaluation is Enterprise-only; self-serve tiers get demo mode. The credit model rewards cheap models and penalises the good ones — an expert-model-heavy workflow will spend 4-5x faster than the headline credit counts suggest. There is no on-premises option below Enterprise. The company behind it is not well documented publicly — no clear funding history surfaced in my research — and the name "AgentX" collides with unrelated products (a customer-support chatbot on Capterra, among others), so cite agentx.so specifically when scoping contracts. The 52+ models and 200+ integrations are vendor claims; I verified a sample, not the full set. And as with any hosted agent platform, long-running autonomous behaviour inherits the failure modes of the underlying models — the platform traces and scores mistakes well, but it does not prevent them.

Verdict

AgentX is the rare agent platform that treats shipping and proving as first-class citizens: a clean visual builder, an evaluation loop that produces actionable failure analysis, transparent credit pricing, and deployment breadth (ten channels, rollback, traces) that most competitors bolt on later. The tier structure undercuts its own thesis by gating the full evaluation program behind Enterprise, and the company's own opacity is a modest risk signal. I rate it 3.7 on the rubric — core functionality depth is strong across build and deploy (40%), value is fair at $49 entry with published credit rates (20%), usability is quick to productive (20%), and the ecosystem is broad while the independent reliability record is thin (20% combined). Teams shipping multi-agent workflows to real users — especially agencies running white-label client work — are the buyers this fits best.

Sources

• https://www.agentx.so/
• https://www.agentx.so/product
• https://www.agentx.so/pricing
• https://www.agentx.so/product/build-agents
• https://www.agentx.so/product/evaluate-agents
• https://www.agentx.so/ai-evaluation
• https://www.agentx.so/about
• https://www.agentx.so/post/how-agentx-charges-for-ai-usage
• https://developers.agentx.so/introduction
• https://ai-marketing-tool.com/agents/agentx
• https://www.capterra.com/p/10019131/AgentX