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Keep a durable record of the goal, divide the work into bounded tasks with testable acceptance criteria, and update progress only after checking the code or environment. At each context boundary, give the next run the original objective and a concise, verified handoff—not just a compressed conversation or an unconfirmed claim that the work is done.
Why long coding sessions lose direction
A broad request is not a plan for sustained work. An agent asked to build a production-quality application may attempt too much at once, hit a context limit mid-implementation, and leave the next run without a dependable account of what happened. The next agent can mistake visible partial progress for completion. Anthropic describes these failure modes in its engineering article on harnesses for long-running agents.
There are three separate problems to manage: keeping the original objective and constraints available, making the next unit of work small enough to assess, and establishing whether the result actually works. Context compaction can make a task fit into a later run, but it does not establish that the task is still aligned or complete. As Anthropic puts it, “However, compaction isn’t sufficient.”
How to keep an AI coding agent on track
- Record the goal outside the transcript. Write down the desired outcome, constraints, and important exclusions in a project file or other durable state the next run can access. Keep the original objective intact as the work is divided.
- Choose one bounded next task. Specify what should change, the expected files or behavior, how to check the result, and what is out of scope. “Implement and test the login error state” is more assessable than “finish authentication.”
- Execute within a manageable context. Have the agent work on that step rather than treating an entire multi-stage project as one uninterrupted task. A clean or budget-limited context can help isolate the work, provided the agent receives the durable goal and verified state.
- Inspect before recording completion. Review the diff and run relevant tests or checks. Record what was verified and the evidence for it; do not count an attempted change or an agent’s statement as proof that it works.
- Update the handoff with the result. Note completed work, verification evidence, remaining tasks, known failures, and the next step. If a check fails, preserve the failure and revise or retry the task instead of marking it complete.
- Restart from state, not assumed continuity. At a context boundary, provide the original goal, the current verified record, and the next bounded task. The new run should not have to infer project status from a long conversation.
What a useful handoff should contain
A handoff is an operational record, not a summary of everything said. Keep it concise enough to scan and specific enough to resume work without treating uncertain claims as facts.
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- Goal and constraints: the unchanged project objective and the requirements that still apply.
- Verified progress: what changed, where it changed, and which checks were run with their outcomes.
- Open work: the remaining bounded tasks and their acceptance criteria.
- Known problems: failed checks, unresolved questions, and any attempted fix that remains unverified.
- Next action: one concrete task with a clear definition of done.
For example, a useful status entry would say that a particular error state was implemented, name the test command that passed, identify a separate failing check as unresolved, and state which behavior to handle next. “Most of the feature is done” does not give the next run enough reliable information to act safely.
Choosing how much harness to use
A lightweight written procedure and an external agent harness can both support long tasks. The practical distinction is how much of the state management and checking is enforced by the workflow. Whichever approach you use, assess whether it preserves the original goal, bounds the next task, retains a compact verified state, independently inspects results, and supports recovery when a step fails.
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| Approach | What it does | Best fit | Key risk to manage |
|---|---|---|---|
| Durable project notes and an explicit handoff | A person or agent maintains the objective, task criteria, evidence, failures, and next step outside the execution transcript. | Work where a simple, visible record and manual review are sufficient. | The record can become stale or completion can be logged without adequate checks. |
| Orchestrated harness | A workflow can assign bounded subtasks, pass task state between runs, and use a separate audit step to inspect the environment. | Work that benefits from repeatable coordination across multiple execution steps or contexts. | Automation does not make an incorrect objective or inadequate verification safe; the resulting state still needs scrutiny. |
The long-horizon agent survey groups harness functions into loops and workflows, context and memory, tools, orchestration, hooks, and verification. Those are useful design dimensions, not a requirement to adopt a particular architecture. See the long-horizon agents survey.
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LongHorizon-Harness frames long-running execution as task-state management: a manager derives a bounded subtask from the original goal and verified state, an executor works in a fresh context, and an auditor independently checks the resulting environment. Its abstract says the system “maintains the task state explicitly outside execution and updates it only with facts independently verified from the environment.” That separation helps prevent an executor’s intent or self-reported progress from becoming the project’s official status.
In a small project, the same separation can be a deliberate review step rather than a fully automated system: one run makes the change, then a person or separate checking process examines the diff and test results before the ledger is updated. The important property is independent evidence, not the number of agents.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What benchmark results do—and do not—show
Published results provide evidence about particular systems and evaluation setups, not a forecast for an ordinary codebase. LongHorizon-Harness authors report Qwen 3.7-Plus with their harness at 80.7% versus 51.8% on WeaveBench, 77.2% versus 69.7% on Terminal-Bench 2.1, and 8.3% versus 2.8% on OSWorld 2.0. These are benchmark-specific comparisons; they do not establish that the workflow will produce the same gains on every project. See the LongHorizon-Harness paper.
The 2025 Context as a Tool paper reports a 57.6% solved rate on SWE-Bench-Verified for SWE-Compressor, its system for managing context. That figure belongs to the paper’s system and benchmark setup; it is not an expected success rate or improvement for everyday coding work. See the CAT paper.
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OneDayAgent reports an overall score of 0.821 with the GLM-5.2 backend across 104 tasks on its AgentIF-OneDay benchmark. Its authors describe verification and repair as ways to expose and recover from some delivery failures, not to guarantee success. The score is not a general measure of coding-task completion. See the OneDayAgent paper.
These results support studying context management, task-state records, and verification, but they do not establish a general percentage by which these practices reduce goal drift across everyday software projects.
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