DSPy is cool from an integrated perspective but as someone who extensively develops agents, there have been two phases to the workflow that prevented me from adopting it:

1. Up until about six months ago, modifying prompts by hand and incorporating terminology with very specific intent and observing edge cases and essentially directing the LLM in a direction to the intended outcome was somewhat meticulous and also somewhat tricky. This is what the industry was commonly referring to as prompt engineering.

2. With the current state of SOTA models like Opus 4.6, the agent that is developing my applications alongside of me often has a more intelligent and/or generalized view of the system that we're creating.

We've reached a point in the industry where smaller models can accomplish tasks that were reserved for only the largest models. And now that we use the most intelligent models to create those systems, the feedback loop which was patterned by DSPy has essentially become adopted as part of my development workflow.

I can write an agent and a prompt as a first pass using an agentic coder, and then based on the observation of the performance of the agent by my agentic coder, continue to iterate on my prompts until I arrive at satisfactory results. This is further supported by all of the documentation, specifications, data structures, and other I/O aspects of the application that the agent integrates in which the coding agent can take into account when constructing and evaluating agentic systems.

So DSPy was certainly onto something but the level of abstraction, at least in my personal use case has, moved up a layer instead of being integrated into the actual system.