A planner and a fresh-context implementer do not share the planner’s unspoken assumptions. I addressed that gap by turning each handoff into a small task contract: it names the goal, allowed and forbidden scope, essential repository context, acceptance checks, disallowed moves and the evidence required to call the task done. Before editing, the implementer restates its understanding so a mismatch can be caught early.
This is an account of one author’s workflow and results, not a controlled study. Its practical value is the method: make the boundary between agents explicit, then check whether the added planning effort pays for itself in your own work.
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Why a vague handoff sent the fix to the wrong exporter
The workflow begins with a planner that reads a repository and backlog, breaks a larger goal into tasks, and assigns each task to an implementer starting in a fresh context. A verifier reviews the resulting diff. That separation can help divide work, but it creates a basic information problem: the implementer cannot rely on context the planner never passed along.
The author’s example began with the request, “Fix the flaky date parsing in the export job.” The repository contained both a legacy CSV exporter and an active JSON exporter. The implementer changed the legacy path, while the flaky test in the active exporter remained. The request named the general job but did not identify the intended target clearly enough.
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The lesson is not simply to write longer prompts. It is to specify the task’s boundaries and evidence in a form that another agent can interpret and check.
What belongs in the task contract
The author describes a structured spec with fields for the work, its boundaries, its verification and its stopping conditions. A compact illustrative shape is:
{
"task_id": "export-date-parsing",
"goal": "Fix flaky date parsing in the active JSON exporter",
"why": "The active exporter has a flaky date-parsing test",
"scope": {
"allowed_paths": ["path/to/active-json-exporter"],
"forbidden_paths": ["path/to/legacy-csv-exporter"]
},
"context": ["The JSON exporter is the active export path."],
"acceptance": [
{
"command": "",
"expected": "The relevant date-parsing test passes"
}
],
"forbidden_moves": [
"Do not add a dependency",
"Do not create a new utility module"
],
"done_signal": "Return the diff and raw acceptance-command output",
"budget": {
"turns": "",
"elapsed_time": ""
}
} This is a pattern to adapt, not a claim that every task needs identical values. In particular, replace the illustrative path and command with real repository paths and a command that can actually be run.
Goal, purpose and scope
task_id identifies the handoff; goal says what outcome is wanted; why supplies its purpose. scope.allowed_paths and scope.forbidden_paths make the intended target and exclusions explicit. In the exporter incident, naming the active JSON path and excluding the legacy CSV path would have addressed the ambiguity directly.
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forbidden_moves covers behaviors that are out of bounds even if they seem like convenient ways to solve the task, such as adding a dependency or creating a utility module. These are instructions, not proof of mechanical access control: a forbidden-path entry by itself does not prevent an agent from editing a file.
Context the planner should not leave implicit
The context field carries repository facts that would be costly for a fresh agent to rediscover, or that might be easy to misread. State facts that matter to choosing the right implementation target, interpreting a test, or avoiding a misleading legacy path. Do not assume that a fresh session has inherited the planner’s exploration.
Acceptance checks and completion evidence
Each acceptance entry pairs a command with an expected result. A real test or validation command makes the success condition more checkable than a general instruction such as “make sure it works.” The done_signal says what the implementer must return, such as the diff and raw command output, so the verifier has evidence to assess rather than a bare assertion of completion.
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The budget field bounds turns and elapsed time. It gives the task a stopping condition, but a limit is not a substitute for scope or acceptance criteria. If the work cannot be completed within the budget, the handoff should make it possible to report that outcome rather than quietly expanding the task.
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Validate the spec before handing it off
The author describes using a short deterministic Python validator rather than another LLM as a gate. It checks that required fields exist, the allowed-path and forbidden-path lists are non-empty, acceptance checks include both command and expectation fields, and at least one forbidden move is specified.
Those checks catch missing structure; they do not establish that a path is correct, that a command works, or that the expected result is meaningful. Human review of the values still matters. Keep the validator focused on conditions it can reliably verify instead of treating schema validity as task quality.
Require an echo before the implementer edits
Before inspecting files or making changes, have the implementer restate the task in its own words. The echo should identify:
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- what it intends to change;
- what it will leave untouched;
- how it will establish that the work is done;
- which forbidden moves it will avoid; and
- any unresolved questions.
Compare that restatement with the contract. If it reveals a mismatch, allow one retry to correct the understanding. If questions remain unresolved, return them to the planner rather than letting the implementer guess and edit first. The echo is an early ambiguity check, not a guarantee that later implementation will be correct.
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Have the verifier rerun the acceptance checks
After implementation, the verifier reviews the diff and reruns the acceptance commands, then compares the resulting evidence with the task’s expected results. This separates the implementer’s report from the check used to decide whether the work is done.
There is an important distinction between written boundaries and enforced ones. The author’s described flow uses scope fields in the contract, but does not establish that every boundary is mechanically enforced by the runtime. If your workflow requires a hard guarantee against edits outside approved paths, you need a separate enforcement mechanism; do not infer one from the presence of forbidden_paths.
What the author reported—and what the figures can show
The author reports logging 120 handoffs before adopting the contract, in spring 2026, then another 120 after six weeks of using it, with the later figures reported in August 2026:
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| Reported outcome | Before contract | After six weeks |
|---|---|---|
| Approved by verifier on first pass | 57% (author’s report, spring 2026; 120 handoffs) | 81% (author’s report, August 2026; 120 handoffs) |
| Scope creep or wrong target | 31% (author’s report, spring 2026; 120 handoffs) | 7% (author’s report, August 2026; 120 handoffs) |
| Gave up or produced nothing useful | 12% (author’s report, spring 2026; 120 handoffs) | 12% (author’s report, August 2026; 120 handoffs) |
The author also reports planner cost rising about 20% per task. Taken at face value, the before-and-after account suggests fewer first-pass failures involving scope or the wrong target, alongside more planning effort; the reported rate of giving up did not change. The sources present the same author’s account, not independent corroboration. They do not provide a public dataset, a detailed measurement protocol or an independent replication, so these figures are not evidence of an industry-wide effect or a controlled causal comparison.
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How to decide whether the pattern pays off in your workflow
The approach targets misunderstandings at the handoff boundary; it does not make an agent capable of work it cannot do. Start small, as the author recommends, with four essentials:
goal: name the intended outcome and target clearly.forbidden_paths: identify paths that must remain untouched.- Acceptance checks: specify runnable commands and their expected results.
done_signal: require completion evidence, including raw output where appropriate.
Then add the echo-back and track outcomes before and after adopting it. Useful measures include first-pass approval, wrong-target or scope-creep changes, retries, unproductive handoffs and the time spent writing specs. Compare the reduction in rework with the added planning cost in your own setting; the author’s account cannot tell you whether that trade-off will be favorable for your tasks.
The author’s compact framing is, “Treat every cold-start handoff like an API boundary, because it is one.” That is a useful design principle: define what crosses the boundary, what is allowed on the other side, and how a receiving agent must demonstrate that it delivered the requested result. A contract can make those expectations clearer; only measurement in your workflow can show whether the clarity is worth its cost.
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