Arize AI (@arizeai) / X

Arize AI

1,582 posts

Arize AI

@arizeai

The AI engineering platform for teams shipping reliable AI agents and LLM applications. Also home to @ArizePhoenix.

San Francisco, CA

Joined January 2020

Pinned
Arize AI
@arizeai
Jun 5
Observe 2026 is a wrap. Yesterday we shared what’s next for Arize AX and our vision for the AI factory for self-improving agents. The focus: helping teams turn production behavior into a repeatable loop for finding issues, investigating root cause, testing fixes, and improving
411K
Arize AI reposted
Aparna Dhinakaran
@aparnadhinak
14h
Article
The Plan Is Code: Trying Out Claude Code's Dynamic Workflows
Dynamic Workflows are Claude Code's newest way of coordinating agents. We have followed the various industry approaches to orchestration and will continue to highlight unique ideas we see in the...
14K
Arize AI
@arizeai
15h
Support escalations get expensive when engineers inherit a ticket with symptoms but no debugging context. At Arize, we rebuilt that handoff with AI agents that gather the evidence first: customer context, traces, logs, eval results, prior tickets, and relevant docs. That gave
How Arize built AI-native support workflows that cut resolution time in half
From arize.com
79
Arize AI
@arizeai
Jun 10
Anthropic's latest and greatest model, Fable, is now available in the prompt playground!
160
Arize AI
@arizeai
Jun 9
A malicious VS Code extension sat in the marketplace for 18 minutes this May, long enough to hit ~6,000 machines and lift their npm, AWS, GitHub, and SSH credentials, plus the config files Claude Code keeps on disk. Here's the catch: in your traces, a credential-harvesting tool
How to detect credential theft in AI agent harness traces
From arize.com
1K
Arize AI reposted
arize-phoenix
@ArizePhoenix
Jun 8
Phoenix just hit 10,000 GitHub stars! Three years ago, Phoenix didn't exist. Arize was a closed-source company. A small team was asked to change that. Catch the full interview with the team who made it happen and where AI observability is going next: arize.com/phoenix-10k
2K
Arize AI
@arizeai
Jun 8
Congratulations to our cofounder @aparnadhinak on being named one of the Top 100 Women in AI. A lot of the hardest work in AI right now starts after the demo works: evals, observability, tracing, debugging, and figuring out how to make production systems improve over time.
100womeninai.com
Aparna Dhinakaran | 100 Women in AI — 100WAI
366
Arize AI
@arizeai
Jun 8
Replying to @arizeai
Thank you to everyone who starred the repo, opened an issue, contributed code, challenged a design decision, or helped another engineer debug a production system. 10,000 stars on @github is a milestone that's possible because of you. But the real story is the community that
42
Arize AI
@arizeai
Jun 8
Replying to @arizeai
📖 Read the full story. The blog gets into how a small team built Phoenix “backwards”: features before infrastructure, support handled by maintainers, and roadmap signal coming directly from GitHub, Slack, and users.
Phoenix at 10,000 stars on GitHub: How an open source AI observability project grew by following...
From arize.com
44
Arize AI
@arizeai
Jun 8
Replying to @arizeai
🔭 Explore the microsite. It pulls the milestone together with the timeline, observability adoption data, community moments, and what comes next for agents, evals, and human review loops.
arize.com
Phoenix at 10,000 Stars
Open-source AI observability and evaluation. Ship on evidence, not vibes.
9
Arize AI
@arizeai
Jun 8
Replying to @arizeai
The team then built OpenInference, which added AI-specific semantics like prompts, messages, retrieval spans, tool calls, model outputs, and eval signals. That makes traces more portable, more inspectable, and more useful across the ecosystem.
12
Arize AI
@arizeai
Jun 8
Replying to @arizeai
OpenTelemetry mattered because AI systems no longer live inside a single application. Today models call tools, agents call agents, and workflows span services, frameworks, languages, and runtimes.
11
Arize AI
@arizeai
Jun 8
Replying to @arizeai
One useful lesson from the team? Meeting developers where the work already happens. That meant Jupyter first, then containers, persistence, Postgres, auth, OIDC, TypeScript, framework integrations, and distributed tracing as the community pushed into production.
6