FoundationaLLM vs. LangSmith Agent Engineering Platform

I feel like LangSmith Agent Engineering Platform is great for complex AI agents built with LangChain that take many steps. It is very useful in projects which requires multiple subagents. We built an AI Answer engine for our product where we use many subagents in LangSmith Agent Engineering Platform for query generation, optimization, summarization, etc.

• Provide detailed end to end traces of our agentic application.
• Systematic procedure to evaluation of the agentic and AI applications.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

• Mention different types of eval factors.
• Create more dynamic dashboards based on data.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

In my opinion the technologies and concepts which I use with LangSmith Agent Engineering Platform like API tracing, prompt management, Prompt Hub and Agent Builder were like pioneer in the market with LangSmith Agent Engineering Platform. There are some scopes of improvements in terms of compatibilities and open-source tooling gaps but overall, it has to be on the list if you are doing AI-engineering at Production level.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info

We used LangSmith Agent Engineering Platform only as of now by reccomendation by our colleague. We just started using AI.

• LangSmith reduces the time we spend troubleshooting agent behavior and helps the team debug faster.
• It helps in trace, debug and evaluate agentic workflows.

Incentivized

Independently Invited by TrustRadius

A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info