@Vtrivedy10: great read on agent architecture in prod but my fave piece is that the team uses Traces to diagnose issues —> propose c…
Summary
The tweet recommends an article on agent architecture in production, highlighting the use of Traces to diagnose issues and implement an iterative improvement loop.
View Cached Full Text
Cached at: 06/12/26, 10:57 AM
great read on agent architecture in prod but my fave piece is that the team uses Traces to diagnose issues —> propose changes —> eval changes in a loop
Traces power agent improvement —> if you can see what happened, then you can understand it, and try to fix it
Similar Articles
@Vtrivedy10: my fave point from here: the earlier you think about your agent as a system that can be measured & improved, the faster…
The author emphasizes the importance of treating AI agents as measurable systems early in development, using evaluations as the primary substrate for improvement and production readiness.
@yoheinakajima: great article, mostly focused on coding agents but applies elsewhere impo. aligns w a lot of my prior thoughts: - agent…
A tweet highlighting key principles for building agent systems, emphasizing scaffolding, memory, and reusable tools, based on an article by Yohei Nakajima.
@bentannyhill: Agent observability is a means to an end: making your agent better. But observability and evals tools have traditionall…
Engine is a new tool that connects agent observability traces to automated fixes and evaluations, closing the agent improvement loop for engineering teams.
I analyzed how 50+ AI teams debug production agent failures and got surprised
Based on interviews with 50+ AI teams, the author highlights that production agent failures often stem from minor prompt or configuration issues rather than deep model problems. The article advocates for adopting software engineering practices like versioning, A/B testing, and experiment tracking to improve reliability.
@jeffreyliu_05: Maybe the best article on building good agents out there
A tweet recommends an article on building good AI agents, implying it is highly valuable for developers.