What I learnt building reliable agents in production?
Domain knowledge is your differentiator.
Recommend building good simulators of the environment your agent will live in to scale these capabilities.
Architecture matters a lot.
How we structure agents i.e. their tools, callbacks, and most importantly: context management, is key.
Balance deterministic code and LLM "magic".
Finding the right balance is hard and it can take a lot of trial and error
Use frameworks, don't rebuild them.
Stand on the shoulders of fast-evolving Agent frameworks like Google's ADK etc.
If you're interested in what me and my team built, check out yorph.ai. It's an agentic data platform that helps you sync across different sources, clean/analyze/visualize data, automatic semantic layer creation, and build version controlled data workflows.
I am the founding engineer so ask away!
Great insights! Totally agree that domain knowledge and solid architecture make or break production agents. The balance between deterministic logic and LLM flexibility is the hardest part in my experience too.
Yes, it's been a lot of trial and error :)
Building reliable agents in production teaches you the importance of clean data, robust testing, clear workflows, monitoring, fail-safes, and continuous optimization to handle real-world unpredictability and ensure consistent performance.
Great post, asreddy.
You're spot on that "context management, is key." This is the part that's a constant struggle.
Since you invited us to "ask away" — how are you approaching this? Are you using vector stores for long-term memory, or have you found a more effective way to manage the short-term context window to keep the agent on track?
Good points. The domain knowledge thing is real. Most of the “agent problems” I see are just the model not knowing the world it’s supposed to work in. One question though. What happens when the agent runs long enough that its understanding starts drifting? That’s usually where things get weird.
Solid breakdown. The domain knowledge point is the one nobody talks about. Most agent failures happen because the LLM has no clue about the actual environment it’s operating in, not because the model is “bad.” Simulators and tight context management are the real moat.
Curious how you’re handling the balance between deterministic logic and model-driven behavior at scale. That line gets messy fast once you throw real users and unpredictable data into the mix.