1000% agree. I am increasingly hesitant to believe Anthropic's continual war drum of "build for the capabilities of future models, they'll get better".
We've got a QA agent that needs to run through, say, 200 markdown files of requirements in a browser session. Its a cool system that has really helped improve our team's efficiency. For the longest time we tried everything to get a prompt like the following working: "Look in this directory at the requirements files. For each requirement file, create a todo list item to determine if the application meets the requirements outlined in that file". In other words: Letting the model manage the high level control flow.
This started breaking down after ~30 files. Sometimes it would miss a file. Sometimes it would triple-test a bundle of files and take 10 minutes instead of 3. An error in one file would convince it it needs to re-test four previous files, for no reason. It was very frustrating. We quickly discovered during testing that there was no consistency to its (Opus 4.6 and GPT 5.4 IIRC) ability to actually orchestrate the workflow. Sometimes it would work, sometimes it wouldn't. I've also tested it once or twice against Opus 4.7 and GPT 5.5; not as extensively; but seems to have the same problems.
We ended up creating a super basic deterministic harness around the model. For each test case, trigger the model to test that test case, store results in an array, write results to file. This has made the system a billion times more reliable. But, its also made the agent impossible to run on any managed agent platform (Cursor Cloud Agents, Anthropic, etc) because they're all so gigapilled on "the agent has to run everything" that they can't see how valuable these systems can be if you just add a wee bit of determinism to them at the right place.
We've got a QA agent that needs to run through, say, 200 markdown files of requirements in a browser session. Its a cool system that has really helped improve our team's efficiency. For the longest time we tried everything to get a prompt like the following working: "Look in this directory at the requirements files. For each requirement file, create a todo list item to determine if the application meets the requirements outlined in that file". In other words: Letting the model manage the high level control flow.
This started breaking down after ~30 files. Sometimes it would miss a file. Sometimes it would triple-test a bundle of files and take 10 minutes instead of 3. An error in one file would convince it it needs to re-test four previous files, for no reason. It was very frustrating. We quickly discovered during testing that there was no consistency to its (Opus 4.6 and GPT 5.4 IIRC) ability to actually orchestrate the workflow. Sometimes it would work, sometimes it wouldn't. I've also tested it once or twice against Opus 4.7 and GPT 5.5; not as extensively; but seems to have the same problems.
We ended up creating a super basic deterministic harness around the model. For each test case, trigger the model to test that test case, store results in an array, write results to file. This has made the system a billion times more reliable. But, its also made the agent impossible to run on any managed agent platform (Cursor Cloud Agents, Anthropic, etc) because they're all so gigapilled on "the agent has to run everything" that they can't see how valuable these systems can be if you just add a wee bit of determinism to them at the right place.