Lessons Learned Building Reliable Multi-Agent Systems

www.yorph.ai

We are getting ready to launch our agentic data platform and wanted to share what we think are the most important things we've learned. Turns out that building a reliable agentic system is largely about good engineering fundamentals and clear written communication. Here are a few lessons that stuck:

Domain knowledge is your differentiator. Whether it's tools, evals, or fine-tuning, your agent's domain knowledge is what sets you apart from being just a wrapper around an LLM. We recommend building good simulators of the environment your agent will live in to scale these capabilities.

Architecture matters. The difference between a flashy demo and a reliable product comes down to how agents are structured, their tools, callbacks, and most importantly: context management. That includes cross-agent instructions, memory, examples. Imagine giving instructions to an intern. You want them to be complete but not overwhelming.

Balance deterministic code and LLM "magic". A good production system finds the middle ground between letting the LLM cook and making sure it doesn't burn down the kitchen. This can take a lot of trial and error to find the right balance.

Use frameworks, don't rebuild them. While it can be a great learning experience to implement your own LLM-call-and-response-parsing while loop from scratch, the frameworks around today can really save you a ton of time and irritation. Stand on the shoulders of fast-evolving Agent frameworks like Google's ADK, and just fork them when you inevitably need them to do something bespoke for your special agent.

It's been a ride getting this ready for production. If you're exploring agentic workflows for data integration, data workflow automation, and analysis, check out what we're building at Yorph AI. We've also got a short demo here showing what our product can do.