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Complete the prerequisite concepts, read the principles and counterexamples, and then explain why in your own words.
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Tool semantics and version migration in long-running tasks
Examine tool Schema version, long-term task compatibility and release rollback.
Knowledge content check2026-10-03 · Check the source of the original question2026-10-02
It is recommended to understand first:
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Select the starting point based on the current basis, or you can go deeper one by one. When you encounter an unfamiliar concept, go back to the core principles first; use the knowledge exercises to check your understanding when you are finished.
Implement next
Prepare to write the principles into code
Understand implementation steps and boundaries, complete small tasks, and check results against acceptance requirements.
Reading implementation and trade-offs →Debug failures
Need to handle failures and changes in conditions
Follow the continuous questioning to locate the failure premise, and then compare the migration cases to explain how the plan should be adjusted.
Continue to delve deeper into the problem →Compare designs
Need to design or review plans
Combine engineering deductions and senior self-evaluation standards to explain the applicable conditions, costs and alternatives of the plan.
Analyze engineering scenarios →Knowledge unit directory
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Core concept · Tool semantics and version migration in long-running tasks
Understand the core principles first
Preparatory concepts:API compatibility, Immutable approval object, Canary release
Compatibility not only means that JSON can be parsed, but also means that the business meaning and result judgment of the old call are maintained. Long-term tasks must know which contract to execute; explicit migration or blocking is not possible when lossless conversion is not possible, and the model cannot be allowed to guess the old and new units and status.
Identical types can hide semantic changes
Changing amount from yuan to fen leaves a number but changes units by 100. A new pending status can parse successfully while old code mistakes every non-failure for success. Contracts include units, time zones, null handling, errors, and effects as well as types.
Runtime selects the contract version
Persist the creation-time contract and parameter digest. Recovery checks continued availability. Implement and record lossless adapters deterministically; changed actions require renewed validation and approval. Arbitrary model-supplied version fields do not ensure compatibility.
Preserve a release recovery path
Coexisting versions, read-only shadow comparisons, and limited canaries reduce risk. Do not duplicate real writes for comparison; shadows produce plans or use isolated environments. Google SRE informs canary observation. Business policy determines retention and migration; MCP does not supply them automatically.
Check understanding with a question
After the tool goes online v2, the old tasks have not been completed yet. How to make the changes in parameters and results compatible?
Persist the contract version at task creation. Optional fields can preserve compatibility; changed semantics, units, or required parameters need explicit versions and adapters or migrations. Validate both requests and results. Continue old tasks under pinned versions or block them explicitly. Regress valid and invalid fixtures before release, retaining old-version availability for rollback.
Implementation and trade-offs
Compatibility is not just about JSON parsing
Changing the amount from "yuan" to "cent", the field is still a number but the semantics have been destroyed; adding an enumeration value to status may also cause the old client to enter the default success branch. Therefore the contract should document units, time zones, null meanings, error codes and side effect semantics. Just because the tool name remains unchanged does not mean that the business behavior has not changed. The running log needs to save the Schema and implementation version.
Select versions for long-running tasks
Bind the tool version and input digest to the running record, and verify during recovery that the pinned version is still callable. If the new version can be adapted losslessly, the adapter converts in deterministic code and records the conversion results; the model cannot be asked to guess the meaning of the fields. If it is incompatible, it will enter the pending migration state, which will be handled by clear migration rules or manually. When it comes to approved actions, recalculate the action digest after migration and check whether the approval is still valid.
Release and rollback
First, let the old and new versions coexist, select read-only traffic or test copies for shadow comparison, and then increase the volume in a small range. Do not execute write tools twice just to compare results. The shadow side only generates action plans or uses an isolated environment. To roll back, you need to restore the tool directory, implement the matching version with the configuration, and ensure that the new tasks that have been created have a processable path. You cannot just roll back the prompt.
Verify acceptance criteria
Contract testing covers missing fields, unknown fields, unit boundaries, abnormal results and old task recovery. Monitor parameter rejection rate, adapter usage and business result differences. Candidates should give the version retention period and a list of unfinished tasks before retiring a version; if it only says "Schema plus a version field" but it is unclear who will select it and when it will expire, the plan is still incomplete.
Engineering deduction
- scene
- Interview hypothesis: The payment tool upgrades the amount unit from yuan to cents, and there are tasks waiting for approval for two days.
- design decisions
- Keep the old version or use explicit adaptation and re-verify the action digest on recovery.
- Verify target
- Old tasks don't pay a hundredfold due to unit changes, and abnormal migrations block instead of guessing.
- applicable boundary
- The examples are used to examine contract governance and do not represent actual payment system implementations.
Continuous questions and answers
Continue reading along with the premises and constraints of the problem. Understand the reference answers first, then try to put away the answers and explain the cause and effect and trade-offs in your own words.
Level 1Why might the newly added enumeration values be incompatible?
Parsing compatibility does not guarantee that old programs make the same business judgments.
Reference answer
The old program may be successfully processed using the exhaustive branch or default, but will falsely report completion after adding pending_review. Strict Schema may also reject new values outright. Define conservative paths for unknown states, and input and output contracts are returned separately; Proto documents also distinguish between wire-format readability and application behavior safety, and JSON tools need to check their own specific consumers.
Follow this answer further
Level 2Is it a safe adaptation to map all unknown states to failed?
Conservative mapping also needs to avoid error-driven retries, and distinguish between final states and unknown states.
Reference answer
At least more conservative than the default success, but may mistake processing for final failure and trigger repeated writes. Prioritize mapping to unidentified or pending verification, prohibit automatic actions that rely on its final state, and read back the state or migrate consumers. Adaptation should preserve the original value and not pretend to be clear after losing the fact.
Follow this answer further
Level 3The new status cannot be recognized for a long time. How to end the old task?
Unknowns cannot hang permanently and require migration and bounded closing paths.
Reference answer
Use clear migration rules to switch to a consumer that can understand the new state, and verify the original operations, parameters, and approvals; if migration cannot be performed, it will end with pending or restricted failure, indicating that the business results have not been confirmed. You can't poll infinitely, and you can't change unknown to successful. The version-retirement plan should scan such tasks in advance.
Level 1How does the shadow test writing tool avoid double writing?
Validating new versions must not create a second real side effect.
Reference answer
Execute a real write only once through the selected version. The shadow version can generate action parameters from the same input and compare them with the selected action, or run against isolated data and a simulated remote service. Separate comparison and production environments and ensure the comparison has no real credentials. Even with a shared idempotency key, old and new versions with different parameters must not compete over that key as an experiment.
Level 1What should I do if there are still tasks when the old version is offline?
Delisting is life cycle management, not just deleting tools from the list.
Reference answer
List the unfinished tasks and side effects that have occurred first to determine whether the old version can be continued, migrated without loss, or requires manual processing. Notify retention periods and explicitly block unavailable versions upon recovery; retain necessary query and reconciliation capabilities. You cannot just delete the directory entry and change the model to another tool. Old approvals and parameters may not apply to the new contract.
Draw inferences from one example: If the conditions change, how to deduce it?
First find out the conditions for change, and then determine which premises in the original plan still hold true. The following cases are teaching deductions to facilitate the transfer of principles to new problems.
Add optional fields
Changing conditions:Incompatible unit changes are replaced by field extensions with unchanged default behavior
Extended question:Is it necessarily compatible?
Derivation and reference solutions
Not necessarily. Confirm whether the old end rejects extra fields, whether the new end maintains the original behavior when fields are missing, and whether downstream serialization and signing are affected. Only the consumers of the new fields on the request side and the new fields on the result side are different and need to be verified separately; old tasks do not need to be migrated after compatibility is determined.
The principles that remain unchanged:Compatibility is based on real consumer behavior, and changing labels cannot replace verification.
Urgent security fix
Changing conditions:Normal coexistence upgrade becomes the old implementation and cannot be continued.
Extended question:Do you still want to insist on running the locked version?
Derivation and reference solutions
Locking the contract does not mean that a vulnerable implementation must be run. If the fix preserves the contract, the implementation can be replaced, the version recorded, and key behaviors regression-tested; if it cannot be maintained, the old tasks can be suspended, in-flight actions verified, and migrated. Security fixes require specific closing rules and cannot silently change the meaning of actions.
The principles that remain unchanged:Version tracing serves explainable execution, and explicit decision-making and evidence are still required when risks change.
Easy to make mistakes
- Only determine compatibility based on field type
- Use the latest tools directly when restoring
- Use model to guess unit conversions
References
It is designed based on public technical information; the reference materials support the technical mechanism, and the scenarios and scoring standards are designed by this website and do not represent the original interview questions of a certain company. New Q&A and migration cases are added for principle explanation, and source verification and case operation verification are recorded separately.
Check how far you understand
After reading, you can explain the principles, boundaries, and trade-offs against these standards. It is up to you to evaluate your mastery; if further verification is needed, complete the small tasks below.
- Basic standards met
- Can point out the risk of semantic changes such as amount units.
- Intermediate and advanced signals
- Task version locking, adaptation and dual-version operation strategies are given.
- Senior criteria
- Full consideration is given to approval invalidation, shadow-free side effects, and rollback of old and new tasks.
Continue to do advanced research experiments
Push the read-only tool boundary to the approval boundary
First observe whether business effects have been generated while waiting for approval, and then press README to check action binding and recovery.
Read full text and fault analysis → · Download Reliability Experiment v3 ↓
python3 approval_cli.py submit
python3 approval_cli.py run
python3 approval_cli.py inspect
# 审查 draft 和 action_json 后，按 README 使用对应 binding_hash 批准并恢复Keep evidence and check item by item
- Status waiting_approval, no reports service has written yet.
- Review actions, targets, scopes, inputs, and content summaries before approving the corresponding binding_hash.
- Run tests to reject, cancel and modify content respectively to explain why old approvals cannot be reused.
Local approval fixture; --actor is not a login or production authentication. No network by default.
Hands-on verificationComplete on demand · Suggestions15 minutes
Design the upgrade process for amount yuan transfer points, and mark the adaptation points and approval checkpoints.
Expand acceptance requirements and checkpoints
- Conversion rules are certain and measurable
- There is a clear version of the tasks to be executed
- Comparison of writing tools does not produce double writing
Key inspections
- Identify structural compatibility and semantic compatibility
- Tool contract used by locked tasks
- Able to describe migration, coexistence and rollback