AgentX

Evaluate AI agent, pinpoint issues, and fix with one click.

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Artificial Intelligence

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Been waiting for something like this. The eval-before-deploy angle is exactly what's missing from most agentic stacks - you can ship a beautiful agent that falls apart on edge cases nobody thought to test. Curious how you handle multi-step tool call chains where the failure happens 3-4 calls deep? That's usually where debugging gets messy and most observability tools lose the thread.

Biggest one for me: the agent itself worked, but a second AI step that summarized the conversation afterward silently dropped fields the user had actually given (a specific detail mentioned mid-call just vanished from the summary). No error, no crash, just quietly incomplete output. That kind of failure is the hardest to catch because everything LOOKS fine until you diff the transcript against the summary by hand. Root-cause analysis on the analysis step itself, not just the main agent, seems like exactly the gap tools like this should close.

This is such a needed tool! we work with a lot of AI agent builders and this is what is missing!! Curious what your GTM roadmap is 😊

Liked the “model sovereignty” point in the video - but fast-moving models are what make that tricky in practice. If evals are tuned to one version, how much actually survives updates?
Curious if switching LLMs here really transfers cleanly, or still means re-tuning the eval layer.

Congrats on the launch!

Hi Team, Interesting Product!!

How would you compare AgentX to n8n?

Both seem to automate workflows, as a n8n user, this would help me in deciding a move.

One thing I've noticed is that agents often fail gradually rather than catastrophically. Can AgentX detect quality drift over time, where outputs are technically correct but becoming less useful or reliable across releases?

The build experience for agents has gotten good across the board — where I see teams get stuck is after launch: knowing whether the agent is actually doing the right thing in production. Do you surface per-conversation traces and a way to flag/replay bad responses, or is evaluation left to the builder? That post-deploy feedback loop is usually what separates a demo agent from one people keep using.

Congrats! observability for agents feels like an emerging category.how do you differentiate from traditional monitoring tools?

Congratulations on the launch!
comparing models across providers is something many teams struggle with.which providers are currently supported?

The combination of evaluation and observability is compelling.both are essential for reliable deployments.which feature receives the strongest feedback from users?

Solid work! IMO the CI/CD framing only holds if the evals are deterministic and an issue could be that agents almost never are. Are you guys gating deploys on a pass rate (like 9/10 runs)? Thanks.

Very relevant problem space.as agents gain autonomy, reliability becomes essential.what metrics do your customers care about most?

the observability angle is compelling.visibility into agent decisions is often limited.can users replay failed execution paths?

Worth noting how much of the multi-agent hype so far has been more marketing than architecture the hierarchical team structure here suggests an actual systems level approach rather than just multiple LLM calls dressed up as agents.

Agent testing is definitely becoming a bigger challenge.especially as workflows become more autonomous.what stage companies benefit most from AgentX?

The productivity unlock from agent coordination tends to be underrated until teams hit the wall of manually stitching together single purpose bots this looks like it's targeting exactly that pain point.

the root cause analysis feature sounds powerful.finding the actual reason behind failures often takes hours.how accurate have the suggested fixes been in practice?