AgentX

AgentX lets you assemble a workforce where an orchestrator agent and its members each hold a defined role, instructions, model, memory, and tools. Instead of one agent doing everything, the team splits the work and the final answer comes from the combined effort. Every decision stays traceable, which helps when a workflow grows past a single step.

You can run agents against test sets before deploying and track regressions across versions. AgentX uses LLM-as-a-judge scoring plus optional cosine and Jaccard similarity metrics to catch hallucinations and broken tool calls. A CI/CD gate blocks or promotes a version based on measured quality, so a bad build doesn't reach users.

Instrument LangChain, CrewAI, OpenAI Agents SDK, Anthropic, or Google ADK once, and every run streams into a single trace pipeline. Tracing records input, output, latency, tool calls, token usage, and a span tree. This turns the agent from a black box into something your team can actually inspect.

AgentX checks live traces against detection patterns or a sampled LLM judge, then reports triage-ready signals without setup work. Runtime evaluations keep an eye on behavior 24/7, so problems surface during normal operation and not only at release time.

Deployments go to API, Slack, web widget, email, or voice in a single step, and each one is versioned. If a release misbehaves, you roll back in a click. Deployment runs where your data has to live, cloud or on premises.

The platform works across major open and closed-source models, so you aren't locked into one vendor. You can point AgentX at your own provider keys for evaluation, tracing, and hosted agents alike. MCP support lets an agent connect to any Model Context Protocol server for extra tools.