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What output_schema does and does not establish
ADK documents output_schema as a way to constrain the structure of the agent’s final output. It accepts several forms, including Pydantic models, primitive lists, dictionaries, and Google Schema. The agent may call tools while it works, and the declared structure is enforced on the final output rather than on every intermediate step.
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That guarantee is about form. A field typed as str will hold text, and a field typed as float will hold a number, but the schema has no way to judge whether the text or number is sensible for the situation. An illustrative model for a timeline of captioned segments might look like this:
from pydantic import BaseModel
class CaptionSegment(BaseModel):
start_seconds: float
end_seconds: float
caption: str
class CaptionTimeline(BaseModel):
segments: list[CaptionSegment]
A response that satisfies this model can still contain a 2.0-second segment whose caption would take much longer to read. Schema validation passes, and the output is wrong.
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The semantic gap, and where Jev fits
The Jev example in the public repository by jimmyliao, jev-storyboard-lab, targets this gap in a video storyboard workflow. An ADK agent produces timeline segments using a shared Pydantic schema. Each generated segment is then passed to Jev, which checks whether the judgment fits its context, such as whether a caption plausibly fits the segment’s duration.
The repository describes Jev as a calibrated decision API rather than a language model, and places it after generation as a quality-control gate. That is the repository author’s description of the sample, not an independently verified vendor statement. The sample also treats Jev as an addition to deterministic schema validation, not a replacement for it. Schema checks still run first, and Jev answers a narrower question: is this content plausible for its context?
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Three layers of checking, compared
Three different kinds of checking are easy to blur together. The table separates them by what each one establishes and what the public sources leave open.
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| Layer | What it checks | What it establishes | What it does not establish |
|---|---|---|---|
Structural validity (ADK output_schema) |
Types and shape of the final output | The output is well-formed for the declared model | Whether the values are plausible for their context |
| Semantic decision check (Jev, as used in the sample) | Whether a generated judgment fits its context, for example a caption against segment duration | A plausibility decision for each checked item, as the sample applies it | Accuracy rates, false-positive or false-negative rates, or how thresholds should be set: not stated in the sample |
| Operational behavior | Latency, cost, failure handling, threshold sensitivity | Behavior you observe in your own deployment | Not stated: the sources give no benchmark figures for latency, cost, or accuracy |
Implementation sequence
The sample suggests a four-stage pattern. The exact call signatures depend on the current Jev package, so the steps below describe the flow rather than a copy-paste recipe.
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- Declare the output model on the ADK agent. Pass your Pydantic model class as
output_schema, so the framework enforces the structure of the final output. - Let the schema check run first. Only pass content that has already been structurally validated to the next stage.
- Send the relevant content and its context to a separate Jev check. In the sample, each segment is checked against its duration. Keep the check outside the agent’s own generation loop so that the decision is made by a separate component.
- Define what your application does with the result. The next section covers the cases that need a rule.
Where state_schema helps, and where it stops
ADK’s state_schema validates certain state writes at runtime. Keys with scoped prefixes, such as user: and temp:, bypass that validation. If your agent writes checked content into a prefixed key, the state schema will not validate that write. Run the Jev check on the content itself, or on the final output, rather than relying on the state schema for it. State validation addresses state integrity. It does not show that the agent’s final answer was reviewed for plausibility.
Deciding what happens when a check fails
The sources do not define a failure policy for Jev. The choices below are implementation decisions you need to make and document for your own application.
- Check fails. Decide whether to regenerate the item, drop it, or stop the run. Record the rejected output and the reason so the failure can be reviewed later.
- Check is uncertain. If the decision API returns a result that falls between your thresholds, route the item to a person or mark it for review rather than treating it as a pass.
- Check is unavailable. Decide in advance whether the pipeline fails closed (no output is published) or fails open (output is published with a flag). For content that goes to viewers without review, failing closed is the safer default, but that choice is yours to make.
- Thresholds. Treat any threshold as something to calibrate against your own examples. The sample does not establish threshold values.
Version and configuration notes
A PyPI search result listed the jev package at version 0.3.0, uploaded September 18, 2026. Check the current project page before you install, because package versions and APIs can change.
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ADK’s Python repository describes its release cadence as roughly bi-weekly. That is a statement in a repository that changes over time, not a stable release promise, so pin the ADK version your project tests against.
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- Unlocked Android phone gives you the flexibility to change carriers and choose your own data plan[2]; it works - Google Fi, Verizon, T-Mobile, AT&T, and other major carriers
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If you load agents from configuration files, read the current ADK configuration guide before relying on its defaults. Built-in config schemas can reject unknown keys, but configuration references can import Python code, and the documented args denylist is off by default. Treat any configuration you did not write as a security boundary.
What is not established
The public evidence covers the pattern, not a supported integration. The sources do not establish Jev’s current API authentication, exact request and response formats, error behavior, rate limits, pricing, or service availability. They also do not show that Jev is a vendor-supported ADK integration, and the Jev example is a community repository rather than evidence of production readiness. Before you write installation or request code, check the official Jev documentation for the current contract.
No accuracy, latency, or cost measurements appear in the sources. Any gain from adding a semantic check has to be measured in your own pipeline.
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