Most AI agents die in staging because developers have no reliable way to measure whether they actually work before pushing to production.

Prototype graveyards are costing engineering teams months

Building an agent is fast. Trusting it enough to ship is not. Without structured evaluation and a clear path from experiment to deployment, teams burn cycles rebuilding the same scaffolding from scratch on every project.

Three tools shipped together to close one gap

Introducing AgentKit, new Evals, and RFT for agents gives developers a structured framework for building production-ready agents, while the new evals suite lets teams run measurable tests against real agent behavior, and reinforcement fine-tuning (RFT) lets models improve directly from task feedback. You define the task, run the eval, and RFT tightens the model on what actually failed, so the output you get is an agent tuned to your specific workflow rather than a general-purpose approximation.

The teams already behind on agent deployment feel this first

• ML engineers maintaining fragile agent pipelines who need a standardized build structure they can test before it breaks in production
• AI product leads responsible for agent accuracy who finally get quantitative eval metrics instead of vibes-based QA
• Fine-tuning specialists who want model behavior shaped by task outcomes, not just labeled examples someone wrote by hand

The combination is specifically pointed at teams that have already built something but cannot confidently scale it.

The production gap in agent development just became the main battleground

OpenAI’s Assistants API and Google’s Agent Development Kit are both pushing hard into the same deployment layer, and the race is no longer about which model is smarter at baseline. If RFT compounds over iterations, teams that adopt it early will be operating with models that are weeks of feedback ahead of competitors still using static fine-tunes.

What you can do with it starting today

• Build agents with AgentKit’s structured scaffolding instead of raw API calls
• Run automated evals against defined success criteria before any deployment
• Apply reinforcement fine-tuning using real task failures as the training signal
• Iterate on agent behavior without rewriting core architecture each cycle

Pricing not listed — check our directory.

One honest constraint before you commit your roadmap

RFT requires well-defined reward signals, so teams without clear task success metrics will spend significant time on evaluation design before the fine-tuning loop pays off.

LangChain covers similar agent scaffolding ground and has a larger existing ecosystem. AutoGen from Microsoft approaches multi-agent coordination differently, with more emphasis on agent-to-agent communication than on eval-driven tuning.

The agent production gap is closing faster than most teams planned for

The tools separating prototype from production are arriving all at once, and the developers who map them now will not have to reverse-engineer their architecture later. We cover tools like this every Friday — subscribe here and we’ll send the best ones straight to you.