AgentX - Evaluate AI agent, pinpoint issues, and fix with one click.
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Evaluate AI agents before they fail. Create test suites, run evaluations, and pinpoint issues before they reach production.
AgentX provides full observability and traceability for your AI agents. AI analysis not only identifies problems but also suggests fixes-like an AI doctor for your agents.
Simulate run your agents across multiple LLM providers to compare performance, cost, and latency, helping you make better decisions about which LLM to go.
Run eval before deploy. Like CI/CD for AI agents.
Replies
Magicam
This tool really hits the point. Now I don't have to waste my money and time for finding a useful AI anymore.
AgentX - Multi-agent and eval framework
@arthur_winston3Â
Thank you, really appreciate it!
That’s exactly the goal - helping teams evaluate agents before committing more time, money, or infrastructure to the wrong setup.
Choosing the right AI agent or model should be based on real performance, not guesswork.
FunBlocks MindMax
This product is amazing. Congrats on its launch!
AgentX - Multi-agent and eval framework
@peng_woodÂ
Thx Wood! Appreciate it!
Nice launch! Comparing performance, latency, and cost across providers from one place sounds incredibly useful :))
AgentX - Multi-agent and eval framework
@himani_sah1Â
Thank you! :))
That’s one of the areas we’re most excited about. Model choice should not be based on hype or guesswork.
Teams need to see how each provider performs on their actual agent workflows - quality, latency, cost, and reliability - before making a switch.
Have you considered adding automated regression testing whenever prompts, tools, or workflows change?
AgentX - Multi-agent and eval framework
@zerotoxÂ
We already do!
Try section “recommended prompt and tools changes” :)
GrowMeOrganic
how AgentX handles non-deterministic agent behavior across repeated evaluation runs. Is there a way to measure consistency?
AgentX - Multi-agent and eval framework
@iamanantguptaÂ
Yes - repeated runs help measure consistency directly: pass rate, output variance, tool-call consistency, and where behavior drifts.
One good run is not enough for agents
AgentX - Multi-agent and eval framework
@iamanantguptaÂ
Yes - repeated runs help measure consistency directly: pass rate, output variance, tool-call consistency, and where behavior drifts.
One good run is not enough for agents
Nas.com
Can AgentX evaluate multi-agent workflows where several agents collaborate and hand tasks between each other?
AgentX - Multi-agent and eval framework
@nuseir_yassin1Â
Yes - that’s one of the main use cases :)
AgentX can evaluate the full multi-agent workflow: handoffs, role boundaries, tool usage, context transfer, intermediate steps, and whether the team completed the intended task.
Multi-agent systems need evals at the coordination layer, not just per-agent outputs
TestMu AI
Congrats on the launch :) The screenshots look clean and the workflow appears straightforward for developers.
AgentX - Multi-agent and eval framework
@syed_shayanur_rahmanÂ
Yep! Developers and non-techies can find themselves pretty easily ;)
Documentation.AI
How does AgentX integrate with existing frameworks like LangGraph, CrewAI, AutoGen, or custom agent architectures?
AgentX - Multi-agent and eval framework
@roopreddy one word: FULLY ;)
AgentX - Multi-agent and eval framework
@roopreddy It is supported via our API. See our developer doc here: https://docs.agentx.so/docs/getting-started
That's extremely helpful. My big pain point today is to build a effective continuous improvement process for my agents. I will give a try, definitely . Congrats on the Launch and count on me as a customer !
AgentX - Multi-agent and eval framework
@alyssoncm Thank you Alysson! Keep me posted and would love to help.
How does AgentX avoid the classic multi-agent failure mode of agents talking past each other duplicating work?
AgentX - Multi-agent and eval framework
@carter_son Good one, this is what kills most "agents in a room" setups.
We handle it by architecture: it's hub-and-spoke. A single Manager reads the task and assigns it to specific specialists, so agents don't self-assign and grab the same job. Each agent has its own scoped role, permissions and knowledge - clear separation keeps them in their lane and stops context explosion. They hand off context and escalate rather than all broadcasting at once.
And since this is the eval launch: we trace both the individual agents and the overall workflow, so if two start stepping on each other it surfaces before production, not in a user complaint.