Arklex AI

Find your agent's errors before your real users do.
Open source：

[Arklex AI @ArklexAI]

Shipping AI agents is easy 🤖

Testing them is harder 🧪

Your agent looks fine… until a user:
• changes their mind mid-conversation
• asks something off-script
• hits a weird edge case 🐛

Most teams write manual test cases.

They take forever and never cover enough.

So we built ArkSim.

It generates synthetic users, runs multi-turn conversations, and finds failures before production users do.

Install in seconds:
pip install arksim 🚀
https://t.co/AlJ5f9kP0E

#AI #AIAgents #OpenSource #AIEngineering

[Arklex AI @ArklexAI]

If you're in NYC for TechWeek on June 3, come find us at Plug and Play NYC's AI Nexus Batch 1 Expo.
We're part of the inaugural NYC cohort, alongside other AI founders working in Enterprise AI. Investors, corporate leaders, and ecosystem partners will be in the room.
Wednesday, June 3 | 10:00 AM - 2:00 PM | NYC
Get on the list: https://t.co/Iu1jpEVglE

[Arklex AI @ArklexAI]

Try ArkSim: https://t.co/hqPFkDFgQh

[Arklex AI @ArklexAI]

Most AI agent failures don’t show up in demos or unit tests.

They appear weeks after launch, when real users push conversations into edge cases your evals never covered.
That’s agent drift.

ArkSim is our open-source framework for testing AI agents with realistic multi-turn synthetic users before production. It helps teams uncover hallucinations, policy failures, context loss, contradictions, and other conversational breakdowns at scale.

The new walkthrough covers simulation, evaluation, and using ArkSim as a CI quality gate for agent systems.

Read the full article featured on All Things Open:
https://t.co/WMt4GWayM8

[Arklex AI @ArklexAI]

@dify_ai Try ArkSim with your Dify agent: https://t.co/hqPFkDFgQh

[Dify @dify_ai]

Dify x Arklex: testing AI agents before they reach production.

We tested the @dify_ai and @ArklexAI integration, which connects ArkSim, Arklex’s open-source agent testing framework, to Dify applications through a lightweight Chat API adapter.

Dify handles workflow design, RAG pipelines, tools, and deployment. ArkSim runs realistic multi-turn synthetic users against the Dify app, helping teams uncover hallucinations, context loss, contradictions, and workflow failures before real users encounter them.

It also supports evaluation metrics such as helpfulness, faithfulness, coherence, and goal completion, making it useful for CI quality gates and knowledge base regression testing.

Read the full walkthrough: https://t.co/bJ8rj8NJDe

[Arklex AI @ArklexAI]

Arklex AI is excited to be part of SGLang Happy Hour: AI Infra in Finance during #NYTechWeek in NYC.

Our Co-founder & CEO Zhou Yu will be joining teams from SGLang, HOF Capital, Crusoe, and Cloudflare for an evening of lightning talks and conversations around the future of inference infrastructure in finance, including latency, reliability, structured outputs, deployment, and production AI systems.
If you’re building in AI infra, inference, or enterprise AI, we’d love to connect.

Wednesday, June 3 | 6:00 PM | NYC
Get on the list: https://t.co/M018m0iPiM

#ArklexAI #SGlang #TechWeek #a16z

[Arklex AI @ArklexAI]

Testing Klarna's Chatbot with a Web Agent: Reliable Under Pressure, Overconfident Without It

We recently ran 20 multi-turn conversations through Klarna's AI customer support chatbot using a Web Agent simulation tool, covering returns, refunds, payment plans, failed payments, order tracking, rewards, and digital gift cards.

What the chatbot says it can help with:
▪️ Payments and refunds
▪️ Order status and tracking
▪️ Klarna account issues

What it actually does:
▪️ Behaves more like a conversational help-center FAQ
▪️ Gives contradictory answers depending on user effort (claimed live tracking in short chats, denied it in longer ones)
▪️ Makes confident claims it has no way to verify (e.g., specific merchants' shipping behavior to Australia)
▪️ Lands precise answers, but usually only after users ask several follow-up questions

Takeaway: Klarna's chatbot presents itself as a full support agent. In practice, it works as a help-center layer that gets sharper the more users push, and overstates what Klarna can actually do when they don't.

Want to test your own agents the same way?

Try ArkSim:
https://t.co/AlJ5f9khb6

full article:
https://t.co/0CAHdmdCLD

[Arklex AI @ArklexAI]

It was a great night co-hosting the AI Agentic Builder Mixer in NYC with Rasa.

We loved bringing together those building and deploying production AI agents, covering everything from voice AI and multi-agent systems to evaluation and reliability.

Huge thanks to everyone who attended. More to come soon.
#Rasa #ArklexAI #AIAgents #VoiceAI #AgenticAI

[Arklex AI @ArklexAI]

If you’re in NYC on Thursday and building AI agents, voice AI, or conversational systems, come join us for a builder-focused social mixer.

Arklex AI and RASA are bringing together founders, engineers, researchers, and AI practitioners working on the next generation of AI agents.

We’ll be talking about:
* Voice AI and realtime agents
* Multi-turn evaluation and testing
* Production deployment challenges
and much more!

🗓️ Thursday, May 14
🕓 6:00 PM
📍 Manhattan (venue shared after registration)

RSVP here: https://t.co/TUcl7ZRfvQ

[Arklex AI @ArklexAI]

Testing Amazon Rufus with a Web Agent: Strong Responses, Fragile Consistency

We recently ran a series of tests on Amazon’s Rufus agent using a Web Agent simulation tool. The goal was simple: evaluate how well Rufus performs in a realistic, multi-turn shopping scenario.

Verdict: Capable, but inconsistent in ways that matter.

Scenario:
Evaluating a hat across warmth, durability, kid fit, budget, plus pros/cons and alternatives.

What worked:
▪️Clear, relevant pros/cons
▪️Recognized it wasn’t suitable for kids
▪️Suggested thoughtful alternatives with reasoning

Where it breaks:
▪️Conflicting answers on the same product (e.g., machine washable vs. not)
▪️Unstable retrieval — sometimes couldn’t find the same product again

Takeaway:
Rufus shows strong reasoning when it works—but inconsistency and retrieval instability limit trust.

Want to test your own agents the same way?

Try ArkSim:
https://t.co/AlJ5f9khb6

Full Article:
https://t.co/YRmJy9zs8c

[Zhou Yu @Zhou_Yu_AI]

Great discussions today at the Agent Conference during our session on “Bottlenecks of AI Agent Engineering Workflow.” I had the chance to host a conversation with Karun Appapogu (Vanguard) and Rama Krishna Raju Samantapudi (ServiceNow) on pain points across the agent lifecycle—from design and implementation to deployment and iteration.

One theme came up repeatedly: the hardest part isn’t building agents, it’s making them reliable.

Getting a demo to work is relatively easy, but making it reliable is much harder, especially when it comes to understanding behavior across real-world scenarios, debugging failures, and iterating with confidence at scale.

This is exactly the problem we’re focused on at Arklex, knowing how systems perform, where they fail, and how to improve them.

👉 Try ArkSim (open-source AI agent testing tool):
https://t.co/ZNQlLCN8bk

#AgenticAI #AIEvaluation #AIEngineering #Arklex #AIConference #OpenSource

[Arklex AI @ArklexAI]

We tested 4 popular AI agent frameworks across 800 adversarial conversations.

We expected a winner. There wasn’t one.

Using the same model (gpt-5.4) across LangChain, CrewAI, OpenAI Agents SDK, and PydanticAI, performance differences were surprisingly small (just a 0.064 spread).

What actually stood out were the shared failure patterns across all frameworks:
- Handling contradictions: 0-10% success
- Resisting unsafe requests under pressure: 0-55% success
- Asking for missing info: 35–75% success

How frameworks differed:
- CrewAI was most concise
- LangChain tracked constraints best
- PydanticAI handled changing requirements well

Important caveat: this test was a chat-only probe which excluded tools, memory, and multi-agent setups, where frameworks actually differentiate.
If you’re choosing a framework based purely on “chat performance”, you’re mostly choosing within noise.

Try it yourself:
👉 https://t.co/AlJ5f9khb6

We’ve open-sourced everything (scenarios, configs, adapters) so you can reproduce or challenge the results.

Full breakdown and methodology:
👉 https://t.co/MWCuXYSZfw

#AIAgent #AIEval #AgentTesting

[Arklex AI @ArklexAI]

You've built your AI agent on Dify. The drag-and-drop workflow is smooth, the RAG pipeline is solid. But how do you know it actually holds up when real users start pushing it in unexpected directions?

That's where ArkSim comes in.

Dify makes it fast to build and deploy production-ready agentic workflows.

ArkSim closes the gap between "it works in testing" and "it works in the wild" automatically simulating thousands of real-world conversations to surface failures before your users do.

Build fast. Ship with confidence.

Try Arksim on your next Dify project: https://t.co/Gg3TV87VKy

#Dify #AIAgent #AgentEval #ArklexAI #OpenSource

[Arklex AI @ArklexAI]

You’ve built your Rasa agent. But no matter how much you test, you can’t predict every path a real user will take. Nobody can.

That’s exactly why Arklex AI and Rasa are teaming up.

Rasa’s CALM architecture gives your agents the flexibility to understand users and the reliability to act predictably. Arksim closes the last mile, automatically running quality tests across thousands of simulated conversations to catch errors before real users ever do.

No more holding your breath at launch.

Try Arksim on your next Rasa project:
https://t.co/pLxccE1t1m

#rasa #AIagent #opensource #AIEval

[Arklex AI @ArklexAI]

Your AI agent didn’t fail. Your testing did.

Wednesday, 9:40 PM.

A PM Slacks: “Still broken. I just rephrased it.”

Engineer checks logs → fixes → redeploys.
Next morning: looks fine.
One more variation… it breaks again.

This loop runs for weeks, sometimes months.
Eventually, it feels “good enough.” You ship.

Three days later, a real user breaks it.

This isn’t a model problem.
It’s a testing problem.

You’re using single-turn testing for a multi-turn, non-deterministic system.

- You test dozens of paths.
- Users explore thousands.
- Every fix risks regression.

So you ship based on a feeling:
“this seems fine now”

What changes everything:
- Run hundreds of multi-turn simulations on every change
- Include rephrasing, edge cases, adversarial inputs
- Turn failures into exact, reproducible paths
- Define metrics: completion rate, fallback rate, tool success

Now “ready” isn’t a guess. It’s a threshold.

You don’t have a model problem.
You have a visibility problem.

The question is simple:
Will you find the failures before your users do?

Read the full article: https://t.co/y6tWHbLeUq

⭐ Try open source and give us a star: https://t.co/JXaVq6hQ57

[Arklex AI @ArklexAI]

Everyone's talking about how easy it is to build AI agents.

The hard part nobody talks about? Evaluation.

In the LLM era, you don't need data to build. You just prompt. But you still need data to know if what you built actually works. The current process: hand your agent to colleagues, collect 100–200 traces, annotate manually, cluster, build an LLM judge. Weeks of work. And your testers are engineers and PMs who don't give you the diverse, edge-case-heavy examples you actually need.

Sound familiar?

We built ArkSim to solve this - an open-source tool that simulates your end users automatically, so you get systematic coverage without the guesswork.

👉 Full read: https://t.co/IksPu0NyHJ

🌟 https://t.co/JXaVq6inUF

[Arklex AI @ArklexAI]

6 months of manual testing. Replaced in 30 minutes.

Jun-shuo (Lance) Liu , a research engineer at Columbia University, was stuck in a cycle most AI agent developers know well - designing test cases by hand, reading through every conversation, writing bug reports, and starting over with every update.

He tried ArkSim. Here's what happened:

→ Test report time: 2–3 days → 30 minutes
→ Iteration cycle: 1–2 weeks → 1–2 days
→ Accuracy: 80% → 90% in one week

But the biggest unlock wasn't speed. It was visibility. ArkSim surfaced a tool selection bug he'd been living with for months. This type of bugs was invisible to manual review, caught in a single run.

He wrote up the full story:
https://t.co/GUDKCLPDhn

#AIAgent #AIEval #AgentTesting