Arize AI

Demos are easy. Production is where reality hits. Join us at Observe to hear from @calcsam, @ivanburazin, @EnoReyes, @Chi_Wang_, and more on what it actually takes to make it work. Grab your spot 👇 arize.com/observe

The agent harness you wrote last year was implicitly tuned for a model that doesn't quite exist anymore. Models shift while we're not looking. Relying on vibes means customers find out before you do. @rachelnabors shares the data and a forkable repo to test your own loop: x.com/rachelnabors/s…

One AI Question with @jimbobbennett What's your 🌶️ take on AI? Our DevEx Engineer's take: Start with the mindset that AI sucks—so you're forced to build the evals and observability to make it great. Don't trust it. Test it. #AI #Programming #SoftwareDevelopment

When a prompt can change tool use, routing, or output without touching application logic, that’s when prompt-as-config starts to matter. Learn more from @dat_attacked: arize.com/blog/prompt-te…

If a prompt change can alter tool use, routing, or output without touching your code, it isn’t just text. It’s runtime behavior. That’s when prompts need their own lifecycle: versioning, rollout, rollback, and observability. This is the decision gate we use:

Agents today are running longer sessions, making more decisions, and touching more systems. That makes knowing if they're doing the right thing critical. Thanks again @furrier and team for having us. ✨

Most agent demos look great. But then things hit prod ... and you realize you have some work to do. Our CEO @jasonlopatecki joined @theCUBE + NYSE Wired to talk about how agents are evolving, and what needs to shift to make them work at scale. 👇 @furrier @GemmaAllenSays @bjbaumann2014

We ran 500 evals to test the "MCP is dead, long live the CLI" claim and presented the results at AI Engineer: Miami. The answer is more interesting than a Twitter fight! Correctness was tied (~82%). But on the hardest analytical tasks, MCP cost 6× more and ran 5× longer than CLI-via-skills. Sometimes MCP was able to one-shot things and beat the CLI, but more often the MCP needed to use the CLI itself to complete a task. Plot twist: a test with NO skills, no MCP, actually did better than MCP and some skills. The real conclusion: MCP vs. CLI is the wrong question. CLI for local, popular, composable, dev-only. MCP for remote, OAuth, proprietary, consumer. Real agents use both. Check out the full talk here: youtu.be/CfITzVcUkZA

Agent traces aren't telemetry. They aren't debugging exhaust. They're the first compounding data loop enterprise software has ever had — and you should make sure you own them. Read the full blog post: arize.com/blog/using-con…

The TLDR from @aparnadhinak? Bigger context windows help. But reliable agents need a harness that decides what stays close, what gets compressed, what gets evicted, and what can be retrieved later. Read more: arize.com/blog/context-m…

Across Pi, OpenClaw, Claude Code, Letta, and Arize’s Alyx, the same techniques keep showing up: • Cap large file reads • Use offset and limit pagination • Budget tool results • Compact older history into summaries • Isolate subagents from parent sessions

Long-running agents don't just need bigger context windows. They need better context management. But context always fills up with more than the task: file reads, tool outputs, stale turns, subagent responses, memory summaries, and repeated previews.

GPT 5.5 and 5.5 Pro are now live in the @OpenAI API and available in the Arize AX prompt playground! Find out how frontier intelligence improves your agents in seconds!

The short version: a harness is the operating layer that turns a model from something that responds into something that can act, observe, adjust, and keep going. Cursor, Claude Code, Windsurf, Codex, and our agent Alyx are all converging on the same pattern. That’s the signal. Get the full breakdown: arize.com/?p=28084&previ…

“If you’re not the model, you’re the harness” sounds clever. It’s also wrong. 👀 A harness isn’t everything around an LLM. It’s a specific architecture that’s showing up in systems that actually work. Our cofounder @aparnadhinak wrote the clearest breakdown we've seen yet.

Check out the full write up: arize.com/blog/ai-agents…

What actually makes an AI agent work in production? Hint: it's not just the model. In an interview, Tobias Leong, CTO and cofounder of Axium Industries, talked through what teams learn once agents leave the demo environment and hit real workflows: missing context, messy source systems, weak evals, and the need to separate retrieval from reasoning.