Evaluate both outcomes and execution traces

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Core concept · Evaluate both outcomes and execution traces

Understand the core principles first

Preparatory concepts:call log, Concurrency dependencies, Permission checks

The outcome answers what the task ultimately became, and the trajectory answers which actions caused that outcome. Acceptance should constrain necessary facts and causal relationships, and allow valid path changes that do not affect the constraints.

Correct outcomes can follow invalid actions

A correct balance may come from another user’s account; a ticket may exist twice. Final snapshots miss such risks, so inspect real tool events against protected resources. Conversely, an expected tool sequence does not establish correct arithmetic.

Express dependencies instead of one fixed sequence

Authorized searches can run concurrently, and caches or databases may provide equivalent versions. Require valid subject-resource-action authorization before each read and evidence within that authorized scope. Do not mandate a database query after a cache hit. Event IDs and dependencies express partial ordering; wall-clock times assist diagnosis.

Preserve uncertainty about missing evidence

A tool-start event is not a completion receipt, and “verified” in prose does not prove a call occurred. Separate insufficient evidence from observed violations and choose release blocking according to risk. The proposed paths and event designs are learning exercises, not executed concurrency experiments.

Check understanding with a question

The final result is correct, does the Agent trajectory still need to be evaluated? How to avoid misjudging different correct paths?

Outcome evaluation establishes what happened; trajectory evaluation checks how, including unauthorized access, duplicates, and waste. Do not require one literal sequence when valid caches or parallel paths exist. Define required and forbidden steps, parameter constraints, and dependencies. Hard violations fail; partial scores aid diagnosis. Real tool events and business records supply evidence, rather than model-written plans.

Implementation and trade-offs

Correct results may mask incorrect execution

The agent might happen to come up with the correct balance but read the wrong account; the report might end up being correct but submit a duplicate ticket multiple times along the way. Only commenting on the final reply cannot detect these issues. The trace provides the real tool name, parameters, return status, retry reason, authorization decision and causal relationship to locate the source of errors and costs. On the other hand, going through the expected steps does not mean that the result is correct, and wrong calculations may still be made after the retrieval is successful. Therefore, result acceptance and trajectory acceptance are complementary and cannot replace each other.

Use allowed paths instead of a single standard sequence

The LangSmith documentation states that exact trajectory has limitations because there may be multiple correct paths. In engineering, rules are divided into must-happen, must-not-happen, parameters must be met, and event A precedes B. For example, authorization must precede reading, reference evidence must come from this permission result, and writing must use the audited operation key. Reads can come from the database or the authorization cache, and two queries that are independent of each other can be parallelized. Comparing a set of tool names will ignore the number of times and parameters; comparing a fixed order will misjudge reasonable parallelism. Partial ordering and status conditions can be closer to real business.

Trajectory collection requires credible events and causal identification

Each event records event_id, run_id, step_id, parent_id, tool, input summary or controlled parameters, start and end time, status and resource usage. The start of the request does not mean that the business is successful. Timeout may mean that the result is unknown. Business reconciliation events should be recorded independently. Sensitive parameters are redacted but resource identifiers that can be used to verify permissions are retained; missing logs cannot be automatically scored. The events of parallel tasks need to be analyzed based on causality, and clock sequencing alone may lead to misjudgment. The "I checked it three times" generated by the model is not evidence of tool execution.

Partial points are used for diagnosis, hard constraints are used for blocking

It can calculate the completion degree of retrieval steps, the number of invalid calls, repeated reading and writing, and the proportion of correct parameters to help locate deviations; prohibited actions such as unauthorized reading and unauthorized sending will still fail directly. Use rules to detect stability boundaries before manually or semantically reviewing open decisions. The following code allows two paths of database reading and cache reading, and checks that authorization precedes reading and prohibits deletion; it only applies to serial list examples. Production parallel tracks should use event DAG to express happens-before, and resource parameters and final business postconditions need to be verified. Don't force redundant calls for "the trajectory is more like the answer", and don't treat unobservable internal thinking as an acceptance log.

code example

Accept two valid traces and refuse to read before authorizing

The Python standard library works. Only serial partial order is demonstrated, authorize does not verify the subject or resources; production must verify parameters, permission decisions, event cause and effect, and business results.

def score(events):
    tools = [e['tool'] for e in events]
    reads = [i for i,t in enumerate(tools) if t in {'db_read','cache_read'}]
    auth = [i for i,t in enumerate(tools) if t == 'authorize']
    return {
        'authorized_before_read': bool(auth and reads) and all(any(a < r for a in auth) for r in reads),
        'no_delete': 'delete' not in tools,
        'has_answer': bool(tools) and tools[-1] == 'answer',
    }
for names in [('authorize','db_read','answer'), ('authorize','cache_read','answer'), ('db_read','authorize','answer')]:
    checks = score([{'tool': n} for n in names])
    print(all(checks.values()))

expected output

True
True
False

Engineering deduction

scene: Assume engineering scenario: Query Agent has two implementation paths: direct database reading and authorized caching.
design decisions: The results both check against the same data snapshot, and track access allows both reads, but requires current authorization to precede access.
Verify target: The acceptance goal is that cache optimization will not be misjudged due to different calling paths, and authorization after reading will still fail.
applicable boundary: The examples do not handle cache staleness, principal and resource authorization matching, or demonstrate concurrent event ordering.

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 1How to determine the causal order of authorization and reading in parallel branches?
The master problem allows parallel valid paths, so causality rather than global sequence numbers is defined.
Reference answer
Record decision_id, principal, resource, action, policy version for authorization decisions and reference it in read events; queue consumption and branches are connected through parent events or causal links. Check that the authorization on the dependency graph was before the read and is still valid, not just compare the timestamps of the two machines. Parallel uncorrelated reads can swap order.
Follow this answer further
1. Level 2After authorization and before reading, permissions are revoked. Is it enough that the dependency order is correct?
  The father question solves the sequence relationship, and the son question increases the status change between the sequence.
  Reference answer
  Not enough. The sequence only proves that it has been checked first, then the valid version or validity period of the authorization must be verified, and the final read gateway performs permission judgment. If permissions and reading cannot be atomically bound across services, the allowed revocation propagation window should be stated; sensitive data is refused to be read when the current permissions cannot be determined.
  Follow this answer further
  1. Level 3An offline trace lacks a revocation version. Does that alone establish an authorization violation?
    The father asks for valid authorization evidence, and then asks how the missing evidence affects the conclusion.
    Reference answer
    A missing field alone cannot be used to assert an actual authorization violation, but neither can it prove compliance. Record unknowns and missing evidence, check whether authoritative authorization audits and read receipts can be completed; block unknowns in release gates for sensitive systems. Report the true violation rate and the unknown proportion separately to avoid lumping the two into one success score.
Level 1What errors will be missed by comparing only the tool name set?
After relaxing the fixed order, the tool set is still insufficient to express safety constraints.
Reference answer
A set of tool names loses call counts, arguments, results, and ordering dependencies: reading twice is the same as reading once, reading tenant A and tenant B are also the same, and the letter is sent first and then approved still contains two tool names. Keep at least the number of calls, resource identity, result status and approval reference, and then use the business status to check the effect.
Level 1When the track log is lost, should it be judged as failed, unknown, or should the release continue?
Double acceptance depends on the quality of collection, and the determination of lack of evidence needs to be clear.
Reference answer
When key permissions or writing evidence are missing, the conclusion is unknown and cannot be passed. Release policies can also include unknowns as blocking items; missing common low-risk performance log samples does not necessarily equate to violations. Keep evidence integrity indicators and reasons, and re-accept after additional inspection of controlled audit or target status.

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.

Caching replaces a database read

Changing conditions:The execution path is shortened, but authorization and data versioning targets are maintained.

Extended question:Should one less query result in trajectory failure?

Derivation and reference solutions

If the cache record also carries the data version, authorization scope and can be verified as not invalid, the query source can be changed. Write the rule as "read the allowed version of the material" rather than "must call the SQL tool"; the final result still needs to be checked for consistency. Cache permissions cannot be relaxed when the permissions are unknown.

The principles that remain unchanged:Allows implementation path changes, retaining resources and authorization constraints.

Two release branches in parallel

Changing conditions:From read-only parallelism to two branches that may be repeated outgoing.

Extended question:Is it enough to just check that there is a final report?

Derivation and reference solutions

Not enough. Both branches should share the same business release key or be coordinated by a unique release status; the trace checks whether two attempts yielded the same effect and checks against the target record. The dependency graph allows for parallel builds, and releases are still bound by approved versions and deduplication.

The principles that remain unchanged:Success is achieved when process constraints and business end states are established at the same time.

Easy to make mistakes

Require unique tool call sequence, penalize valid alternative paths
Compare only the tool name, ignoring parameters, times and side effects
Treat tool request initiation as business success
Treat model readme as real execution trace

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: A correct result may still have involved dangerous or unnecessary actions.
Intermediate and advanced signals: Check trajectories in order of allowed and prohibited actions and cause and effect.
Senior criteria: Tolerate multiple correct paths, measure trajectory rule false positives and calibrate with counterexamples.

View verification records for independent examples

Continue to do advanced research experiments

Let reviews read events and actual effects

Differentiate between run success, result contracts, and unscored semantic quality along actual checkpoints, events, and servicer receipts for the same research task.

Read full text and fault analysis → · Download Reliability Experiment v3 ↓

python3 cli.py memory-put
python3 cli.py submit
python3 cli.py run
python3 evaluate.py
python3 -m unittest discover -s . -p test_lab.py -v

Keep evidence and check item by item

Explain the basis of passed in terms of events and checkpoints.
Point out credible observations of scope, references, idempotent effects, and step budgets respectively.
Semantic_support and model_quality are not_scored, and passed cannot be written as model quality score.

By default, deterministic summary functions and synthetic documents are used; real operating trajectories and mechanical constraints are evaluated, and there is no semantic support to verify the real model.

Hands-on verificationComplete on demand · Suggestions15 minutes

Compare two different but legal instrumental trajectories with an override trajectory that turns out to be correct.

Expand acceptance requirements and checkpoints

Valid alternative paths are not misjudged
Ultra vires will not be released just because the result is correct.
The rules have a clear scope of application

Key inspections

Measurement value that can differentiate between business results and execution processes
Can explain the misjudgment of valid alternative paths by accurate trajectory matching
Can use partial ordering to express dependencies such as authorization before protected reads
Can identify parameter errors, missing logs and untrustworthy readme reports

Which step do you want to learn from this knowledge point?

Understand first

Implement next

Debug failures

Compare designs

Knowledge learning and personal records

Understand the core principles first

Correct outcomes can follow invalid actions

Express dependencies instead of one fixed sequence

Preserve uncertainty about missing evidence

Check understanding with a question

The final result is correct, does the Agent trajectory still need to be evaluated? How to avoid misjudging different correct paths?

Implementation and trade-offs

Correct results may mask incorrect execution

Use allowed paths instead of a single standard sequence

Trajectory collection requires credible events and causal identification

Partial points are used for diagnosis, hard constraints are used for blocking

code example

Accept two valid traces and refuse to read before authorizing

Engineering deduction

Continuous questions and answers

Draw inferences from one example: If the conditions change, how to deduce it?

Caching replaces a database read

Two release branches in parallel

Easy to make mistakes

References

Check how far you understand

Continue to do advanced research experiments

Let reviews read events and actual effects

Keep evidence and check item by item

Hands-on verificationComplete on demand · Suggestions15 minutes

Key inspections

Which step do you want to learn from this knowledge point?

Understand first

Implement next

Debug failures

Compare designs

Knowledge learning and personal records

Understand the core principles first

Correct outcomes can follow invalid actions

Express dependencies instead of one fixed sequence

Preserve uncertainty about missing evidence

Check understanding with a question

The final result is correct, does the Agent trajectory still need to be evaluated? How to avoid misjudging different correct paths?

Implementation and trade-offs

Correct results may mask incorrect execution

Use allowed paths instead of a single standard sequence

Trajectory collection requires credible events and causal identification

Partial points are used for diagnosis, hard constraints are used for blocking

code example

Accept two valid traces and refuse to read before authorizing

Engineering deduction

Continuous questions and answers

Draw inferences from one example: If the conditions change, how to deduce it?

Caching replaces a database read

Two release branches in parallel

Easy to make mistakes

References

Check how far you understand

Continue to go deeper

Continue to do advanced research experiments

Let reviews read events and actual effects

Keep evidence and check item by item

Hands-on verificationComplete on demand · Suggestions15 minutes

Key inspections