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Harness Design for Long-Running Application Development

anthropic.com

Last highlighted March 30, 2026 Source ↗ ← All highlights

First is that models tend to lose coherence on lengthy tasks as the context window fills (see our post on context engineering).
Some models also exhibit “context anxiety,” in which they begin wrapping up work prematurely as they approach what they believe is their context limit.
Context resets—clearing the context window entirely and starting a fresh agent, combined with a structured handoff that carries the previous agent’s state and the next steps—addresses both these issues.
second issue, which we haven’t previously addressed, is self-evaluation. When asked to evaluate work they’ve produced, agents tend to respond by confidently praising the work—even when, to a human observer, the quality is obviously mediocre.
This problem is particularly pronounced for subjective tasks like design, where there is no binary check equivalent to a verifiable software test. Whether a layout feels polished or generic is a judgment call, and agents reliably skew positive when grading their own work.
However, even on tasks that do have verifiable outcomes, agents still sometimes exhibit poor judgment that impedes their performance while completing the task. Separating the agent doing the work from the agent judging it proves to be a strong lever to address this issue.
The separation doesn’t immediately eliminate that leniency on its own; the evaluator is still an LLM that is inclined to be generous towards LLM-generated outputs. But tuning a standalone evaluator to be skeptical turns out to be far more tractable than making a generator critical of its own work, and once that external feedback exists, the generator has something concrete to iterate against.
Second, by separating frontend generation from frontend grading, we can create a feedback loop that drives the generator toward stronger outputs. With this in mind, I wrote four grading criteria that I gave to both the generator and evaluator agents in their prompts: • Design quality: Does the design feel like a coherent whole rather than a collection of parts? Strong work here means the colors, typography, layout, imagery, and other details combine to create a distinct mood and identity. • Originality: Is there evidence of custom decisions, or is this template layouts, library defaults, and AI-generated patterns? A human designer should recognize deliberate creative choices. Unmodified stock components—or telltale signs of AI generation like purple gradients over white cards—fail here. • Craft: Technical execution: typography hierarchy, spacing consistency, color harmony, contrast ratios. This is a competence check rather than a creativity check. Most reasonable implementations do fine here by default; failing means broken fundamentals. • Functionality: Usability independent of aesthetics. Can users understand what the interface does, find primary actions, and complete tasks without guessing? I emphasized design quality and originality over craft and functionality. Claude already scored well on craft and functionality by default, as the required technical competence tended to come naturally to the model. But on design and originality, Claude often produced outputs that were bland at best. The criteria explicitly penalized highly generic “AI slop” patterns, and by weighting design and originality more heavily it pushed the model toward more aesthetic risk-taking.
With these findings in hand, I applied this GAN-inspired pattern to full-stack development. The generator-evaluator loop maps naturally onto the software development lifecycle, where code review and QA serve the same structural role as the design evaluator.
In our earlier long-running harness, we had solved for coherent multi-session coding with an initializer agent, a coding agent that worked one feature at a time, and context resets between sessions. Context resets were a key unlock: the harness used Sonnet 4.5, which exhibited the “context anxiety” tendency mentioned earlier. Creating a harness that worked well across context resets was key to keeping the model on task. Opus 4.5 largely removed that behavior on its own, so I was able to drop context resets from this harness entirely. The agents were run as one continuous session across the whole build, with the Claude Agent SDK’s automatic compaction handling context growth along the way.
Planner: Our previous long-running harness required the user to provide a detailed spec upfront. I wanted to automate that step, so I created a planner agent that took a simple 1-4 sentence prompt and expanded it into a full product spec. I prompted it to be ambitious about scope and to stay focused on product context and high level technical design rather than detailed technical implementation. This emphasis was due to the concern that if the planner tried to specify granular technical details upfront and got something wrong, the errors in the spec would cascade into the downstream implementation. It seemed smarter to constrain the agents on the deliverables to be produced and let them figure out the path as they worked. I also asked the planner to find opportunities to weave AI features into the product specs.
Generator: The one-feature-at-a-time approach from the earlier harness worked well for scope management. I applied a similar model here, instructing the generator to work in sprints, picking up one feature at a time from the spec. Each sprint implemented the app with a React, Vite, FastAPI, and SQLite (later PostgreSQL) stack, and the generator was instructed to self-evaluate its work at the end of each sprint before handing off to QA. It also had git for version control.
Evaluator: Applications from earlier harnesses often looked impressive but still had real bugs when you actually tried to use them. To catch these, the evaluator used the Playwright MCP to click through the running application the way a user would, testing UI features, API endpoints, and database states. It then graded each sprint against both the bugs it had found and a set of criteria modeled on the frontend experiment, adapted here to cover product depth, functionality, visual design, and code quality. Each criterion had a hard threshold, and if any one fell below it, the sprint failed and the generator got detailed feedback on what went wrong.
Before each sprint, the generator and evaluator negotiated a sprint contract: agreeing on what “done” looked like for that chunk of work before any code was written. This existed because the product spec was intentionally high-level, and I wanted a step to bridge the gap between user stories and testable implementation. The generator proposed what it would build and how success would be verified, and the evaluator reviewed that proposal to make sure the generator was building the right thing. The two iterated until they agreed. Communication was handled via files: one agent would write a file, another agent would read it and respond either within that file or with a new file that the previous agent would read in turn. The generator then built against the agreed-upon contract before handing the work off to QA. This kept the work faithful to the spec without over-specifying implementation too early.
I wrote the following prompt to generate a retro video game maker:

Create a 2D retro game maker with features including a level editor, sprite editor, entity behaviors, and a playable test mode. The table below shows the harness type, length it ran for, and the total cost. Harness Duration Cost Solo 20 min $9 Full harness 6 hr $200 The harness was over 20x more expensive, but the difference in output quality was immediately apparent.
Out of the box, Claude is a poor QA agent. In early runs, I watched it identify legitimate issues, then talk itself into deciding they weren’t a big deal and approve the work anyway. It also tended to test superficially, rather than probing edge cases, so more subtle bugs often slipped through. The tuning loop was to read the evaluator’s logs, find examples where its judgment diverged from mine, and update the QAs prompt to solve for those issues.
The logical next step was to find ways to simplify the harness without degrading its performance. This was partly common sense and partly a function of a more general principle: every component in a harness encodes an assumption about what the model can’t do on its own, and those assumptions are worth stress testing, both because they may be incorrect, and because they can quickly go stale as models improve.
In my first attempt to simplify, I cut the harness back radically and tried a few creative new ideas, but I wasn’t able to replicate the performance of the original. It also became difficult to tell which pieces of the harness design were actually load-bearing, and in what ways. Based on that experience, I moved to a more methodical approach, removing one component at a time and reviewing what impact it had on the final result.
As models continue to improve, we can roughly expect them to be capable of working for longer, and on more complex tasks. In some cases, that will mean the scaffold surrounding the model matters less over time, and developers can wait for the next model and see certain problems solve themselves. On the other hand, the better the models get, the more space there is to develop harnesses that can achieve complex tasks beyond what the model can do at baseline.
With this in mind, there are a few lessons from this work worth carrying forward. It is always good practice to experiment with the model you’re building against, read its traces on realistic problems, and tune its performance to achieve your desired outcomes. When working on more complex tasks, there is sometimes headroom from decomposing the task and applying specialized agents to each aspect of the problem. And when a new model lands, it is generally good practice to re-examine a harness, stripping away pieces that are no longer load-bearing to performance and adding new pieces to achieve greater capability that may not have been possible before.
From this work, my conviction is that the space of interesting harness combinations doesn’t shrink as models improve. Instead, it moves, and the interesting work for AI engineers is to keep finding the next novel combination.