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.
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.