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Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →A fresh-context implementer can only act on what the planner makes explicit. I reduced wrong-target edits and scope creep in my own workflow by treating each handoff as a small interface contract: state the goal, boundaries, repository context, acceptance checks, and required completion evidence, then ask the implementer to echo its understanding before it changes anything.
Why a planner-to-implementer handoff needs a contract
In this workflow, a planner reads the repository and backlog, breaks a larger goal into tasks, and assigns each task to an implementer that starts with fresh context. A verifier reviews the resulting diff. The implementer does not inherit the planner’s implicit knowledge: if the handoff leaves a detail unstated, the worker may choose a plausible but incorrect interpretation.
That happened with a request to “Fix the flaky date parsing in the export job.” The repository had both a legacy CSV exporter and an active JSON exporter. The implementer changed the legacy exporter, while the flaky test in the active exporter remained. The task had named a problem but not the intended target clearly enough.
The lesson I took from it was: “Treat every cold-start handoff like an API boundary, because it is one.” A contract does not make an agent more capable; it makes the intended work and the evidence of completion less dependent on assumptions.
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What to put in the task spec
Keep the contract structured and short enough to use routinely. Each field should resolve a real handoff failure rather than add ceremony.
| Field | What it communicates | Failure it is meant to prevent |
|---|---|---|
task_id |
A stable identifier for the task. | Confusing this work with another task or its evidence. |
goal |
The specific result to produce, including the intended target when relevant. | Solving a nearby but wrong problem, such as editing the legacy exporter instead of the active JSON exporter. |
why |
The purpose behind the change. | Making a locally plausible change that misses the reason the task exists. |
scope.allowed_paths |
Paths the implementer is expected to work in. | Unbounded edits or uncertainty about where a change belongs. |
scope.forbidden_paths |
Paths that must not be changed. | Scope creep and edits to the wrong implementation. |
context |
Repository facts a fresh worker would otherwise have to rediscover, such as which exporter is active. | Guessing based on incomplete or costly-to-reconstruct context. |
acceptance |
Checks pairing a real command with its expected result. | Ambiguous claims of success that cannot be checked. |
forbidden_moves |
Behaviors to avoid, such as adding dependencies or creating a new utility module. | Unwanted implementation choices that satisfy the goal but violate project constraints. |
done_signal |
The evidence the implementer must return, such as raw command output and a summary of changed files. | Completion claims without enough information for verification. |
budget |
A bound on turns and elapsed time. | Unbounded effort on a task that may need clarification or decomposition. |
Scope fields are instructions unless a separate runtime mechanism enforces them. Merely listing a path under forbidden_paths does not, by itself, prevent an agent from editing that file. Make this distinction explicit when designing the workflow.
Validate the contract before sending it
I use a short deterministic Python validator rather than another LLM gate. Its job is to catch malformed or incomplete specs before they reach an implementer, not to decide whether the task itself is wise.
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- Check that required fields are present.
- Require non-empty allowed-path and forbidden-path lists.
- For each acceptance check, require both a command and an expected result.
- Require at least one forbidden move.
This validation makes the structure predictable. It cannot establish that a command is correct for the repository or that the expected result fully proves the requested change; those still require human judgment and later verification.
Require an echo before implementation
Before inspecting files or editing, ask the implementer to restate its understanding in its own words. The echo should identify the intended changes, exclusions, definition of done, forbidden moves, and any open questions. This turns a hidden interpretation into something the planner can review before work begins.
- Send the task contract to the fresh-context implementer.
- Ask for an echo of the goal, target, boundaries, completion evidence, and unresolved questions before any file inspection or edits.
- Compare that echo with the contract. If it reveals a mismatch, allow one retry with clarification.
- Return unresolved questions to the planner rather than letting the implementer guess.
- Once the understanding matches, let implementation proceed within the stated scope.
The echo is an early ambiguity check, not proof that the eventual implementation is correct. It is useful precisely because it surfaces a misunderstanding while it is still cheap to correct.
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Define completion so a verifier can check it
After implementation, the verifier should rerun the acceptance commands and compare the returned evidence with the expected results. This separates an implementer’s claim from an independent check of the work. Ask for raw output rather than a bare “passed,” and make the expected result concrete enough to judge.
For example, a spec might say to run the repository’s relevant date-parsing test command and expect the flaky test in the active JSON exporter to pass. Use the actual command and expected output applicable to the repository; the example is not a universal command. If the check fails, the verifier has actionable evidence to return with the diff.
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In my own account, I logged 120 handoffs in spring 2026 before adopting the contract. I reported 57% approved by the verifier on the first pass, 31% involving scope creep or the wrong target, and 12% that gave up or produced nothing useful. After six weeks using the contract, I reported the same sample size: 81% approved on the first pass, 7% scope creep or wrong target, and 12% gave up or produced nothing useful. I also reported that planner cost rose about 20% per task.
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| Reported outcome | Before contract | After six weeks |
|---|---|---|
| First-pass verifier approval | 57% (author’s report, 120 handoffs, spring 2026) | 81% (author’s report, 120 handoffs, August 2026) |
| Scope creep or wrong target | 31% (author’s report, 120 handoffs, spring 2026) | 7% (author’s report, 120 handoffs, August 2026) |
| Gave up or produced nothing useful | 12% (author’s report, 120 handoffs, spring 2026) | 12% (author’s report, 120 handoffs, August 2026) |
| Planner cost per task | Baseline not stated | About 20% higher, according to the author; measurement method not stated |
These are my reported results, not an independently validated study. The account does not provide a public dataset, a detailed measurement protocol, or independent replication, so the figures should not be treated as industry-wide effects or proof that the contract alone caused the change. The unchanged 12% “gave up” rate also points to the pattern’s boundary: it targets misunderstandings and wrong-scope work, not tasks an agent cannot complete.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to try the pattern in your own workflow
Start with four fields
For a lightweight first version, specify goal, scope.forbidden_paths, acceptance checks with real commands, and a done_signal that requires raw output. Name the target and exclusions as concretely as the task requires; do not assume the worker knows which implementation is active.
Add context, boundaries, and echo-back
When a fresh worker would need repository knowledge to choose correctly, put that knowledge in context. State permitted scope and forbidden moves, then require the pre-work echo. Keep open questions visible and route unresolved ones back to the planner.
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Measure whether the overhead pays for itself
Track first-pass approvals, wrong-target or scope-violation changes, retries, and the time spent writing specs. The relevant comparison for your team is whether reduced rework offsets the added planning cost. The reported 20% increase is the author’s estimate, not a universal overhead figure.
When adapting this approach, look at whether scope is only written down or mechanically enforced, whether acceptance checks can be rerun independently, whether ambiguity returns to the planner before edits, and whether a task budget triggers a stop or signals that the work should be decomposed. Treat these as design choices to evaluate in your own setup, not as proven features of the contract.
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