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AI agents can reason and interact with the rest of your tech stack through tool calls. But how do you wrangle non-deterministic tool calling in a way that is safe for production, without rebuilding your existing agent from the ground up?

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Not-so-hot take: you need to measure your agent's performance in production.

Maybe-a-little-hotter-take: you should be using production data for evaluation rather than synthetic events.

Enter Online and offline evals; one measures performance in real time. The other, off the execution path, either before deployment, or after the agent run has happened. When to use both is explained in our latest guide!

`Exclude` has saved me more times than I can count, good to see it's not just me that reaches for it when fixing type bugs!

Codex 5.3 vs Claude Opus 4.6, who builds the better full-stack app?

I had both models build the exact same Next.js + TypeScript project,

An AI Job Application Analyzer that scores resumes against a job description and returns: match score (0–100) matched + missing skills tailored resume bullet rewrites interview questions

Langsmith just released their new AI powered agent builder platform. It allows you to write a prompt and build an AI automation out of it.

N8N, which is a very mature automation platform, also has a similar feature where you can type in a prompt to build an automation.

Lets build the same agent in both platforms using only an AI prompt see which tool works better.

Hi! Great question!

For this round, I decided to compare the (arguably) best models and tools with the most ongoing support and development.

That being said, I love local models! I'm a big fan of Google Gemma running on Ollama, then patched into Aider CLI; I've had excellent luck with that setup.

Maybe round 2 needs to be OSS models!

Hi there! Even though it didn't score the highest (Cursor did!), I loved Claude's code, it's the one that I'm still using after completing the testing! Anthropic got the UX right in my opinion!

Gemini surprised me, too! It was a mixed bag, as it performed well in the production tests but failed significantly on the control test. I bet as the model improves, it will quickly catch up, because that context window is a good feature!

Hey, I'm Mitch (post author).

I'm a 15+ year full-stack engineer who contracts with Render and guest-posted my research for their blog.

Up until recently, I was very skeptical of AI coding tools. My AI usage was basically GH Copilot's autocomplete. After cleaning up too many agent mistakes in production, I set up a structured trial in my real projects to measure what these agents can do under real constraints.

I decided to run two sets of experiments: vibe coding a new application from scratch as a control test, then giving the agents real production tasks. For the production tasks, I gave them backend challenges like building a k8s pod leader election system in Go microservices, and building out CSS templates in Astro.js.

I evaluated Cursor, Claude Code, Gemini CLI, and OpenAI Codex across setup friction, # of follow up prompts, code quality, UX, and context handling.

Cursor won but it was close. I really liked the Claude Code UX and will try the new Cursor CLI. I plan to run a similar benchmark in the fall using newer features like parallel agents and newer models (maybe GPT-5 or whatever comes next).

Let me know what I should test for round 2 or nitpick the criteria I used. The best tool for you might not be the best tool for me, so I encourage you to run your own experiments. Also happy to answer questions about my methodology.