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Reliable agent workflows need more than valid JSON: each model response, tool call, tool result, and application-facing response must have a clear contract, and the application must handle refusals, incomplete output, and execution failures explicitly. Start by defining who consumes each object, constrain its shape where the platform supports it, then test the full sequence—including recovery—not just whether a response parses.
Start with the consumer and the job of each JSON object
Before choosing keys, identify who will read each object: the model, your application, a downstream API, or a user-facing renderer. Those consumers have different needs. A model-facing tool argument might need a small set of permitted inputs; a downstream API response may need pagination and stable identifiers; a renderer may need safe, user-ready text. Combining all of those purposes in one object can make the contract harder to validate and expose fields to consumers that do not need them.
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For each contract, document its purpose, field meanings, required keys, permitted values, and what the consumer should do with the result. Give important fields clear names and descriptions. OpenAI’s Structured Outputs guide recommends clear schema names and descriptions and says to evaluate schema designs rather than assume that a schema is good merely because it validates.
Separate contracts by boundary
- Model response: the structured result the model should produce for the current task.
- Tool arguments: the proposed inputs for one named operation.
- Tool result: the output returned by application code after it executes that operation.
- Public API response: the stable shape your own clients consume, including success or error semantics.
These boundaries may share concepts, but they should not be assumed to be interchangeable. In particular, the model proposes tool arguments; application code remains responsible for validating and executing them.
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Constrain model output without mistaking conformance for completion
When supported by the chosen API and model, schema-constrained output can limit the response to a defined structure, including required keys and allowed values. OpenAI describes Structured Outputs as ensuring responses adhere to a supplied JSON Schema. That constraint is useful for preventing omitted required fields or invalid enum values, but it does not establish that the task was completed, that the content is correct, or that an external operation succeeded.
Handle response status as well as response content. OpenAI’s documentation describes refusal and token-limit truncation cases in which a structured response may not match the requested schema; its examples check for refusal and incomplete response status. The consumer should branch on these outcomes instead of forwarding a partial or refused result as if it were complete.
Design for the schema mode you actually use
OpenAI recommends strict function calling. For its strict function mode, every object must set additionalProperties to false, and every declared property must be required. This affects how optional values are represented: if a field may have no value, the schema may need to require the field while permitting an explicit null value, where that form is supported.
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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Do not assume that every JSON Schema feature is accepted by every endpoint or model. Check the supported subset for the exact combination you deploy. These are platform-specific constraints, not a guarantee of portability between providers.
Illustrative contract sketch
This example shows the design idea, not a claim that the schema syntax is accepted unchanged by a particular API:
{
"operation": "lookup_order",
"arguments": {
"order_id": "A123",
"include_history": false
}
}Document what an order identifier means, whether history can be requested, and what a valid result or error looks like. A parser can confirm that these keys and value types are present; it cannot confirm that the identifier belongs to the current user or that the requested operation is authorized.
Make tool calls explicit, bounded, and executable
A tool call is a handoff, not merely a JSON object in a model answer. In OpenAI’s documented flow, the application sends available tools, receives a proposed call, executes application-side code using the call’s input, sends the tool output back in association with that call, and then receives a final response or another call. Tool output can be structured JSON or plain text.
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For each tool, specify its purpose, argument schema, expected result, and error behavior. Keep the model’s proposal separate from execution: validate arguments and apply your application’s authorization, business rules, and safety checks before invoking the underlying operation. Do not treat schema conformance as authorization.
- Describe the available operation. Use a distinct name and a description that makes its purpose and limits clear.
- Validate the proposed arguments. Check types and permitted values, then apply application-side checks such as access control and business constraints.
- Execute in application code. Handle downstream failures and return a result or a defined error outcome.
- Associate the result with the call. Return the output for the specific tool call so the model can continue with the right context.
- Handle the next outcome. The model may produce a final response or request another tool; your workflow should support the intended sequence.
Strict function mode can make the argument shape more dependable, but it does not remove the need to validate inputs or define how a failed operation is represented.
Define success, refusal, incomplete output, and errors separately
A successful JSON parse is only one check. Your application should distinguish a completed response from a refusal, incomplete output, invalid content, and a tool or downstream-service failure. Define what happens in each case: retry when appropriate, ask for clarification, return a controlled error, or stop the workflow. Do not pass partial results into later steps as though they were complete.
For application APIs, Google’s JSON style guidance describes a top-level response organized around data or error, with documented error codes and messages. It also presents pagination and continuation fields. Adopt a consistent success/error convention that fits your API, and document which fields may be absent; avoid response shapes where consumers must guess whether a mixture of success and error fields means partial success.
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- Refusal: the model declined the request; it is not an ordinary empty success response.
- Incomplete model output: the response did not finish, such as when it was cut off by a token limit; do not treat it as a complete object.
- Invalid model content: parsing or validation failed; decide whether to reject, retry, or request a corrected response.
- Tool or API error: application-side execution failed; return a defined error outcome rather than inventing a successful result.
Make identifiers, timestamps, and pagination unambiguous
Stable identifiers and clear time semantics make it easier for clients and workflow steps to match records and interpret events. Google’s JSON style guide describes context as a client-supplied value echoed by the server for request-response correlation, while an id is assigned by the service. It recommends RFC 3339 for date property values and ISO 8601 for duration values.
For an agent workflow, document what each timestamp represents—such as event time, request time, or update time—and specify timezone and precision. A date string without those semantics can be syntactically valid yet interpreted differently by different consumers.
Pagination also needs an explicit contract. State whether clients use page indexes or a cursor/continuation value, what the continuation field means, and how clients know they have reached the end. Google’s guide includes examples with totals, page indexes, next or previous links, and continuation fields; choose a coherent approach for the API rather than mixing conventions without explanation.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Evaluate the workflow, not just the JSON parser
Build an evaluation set around the behaviors that matter to the agent, then add edge cases. Google’s agents-cli Evaluation Guide lists measures such as tool-use quality, multi-turn tool-use quality, trajectory quality, task success, hallucination, and grounding, with metric choices depending on the agent type. Its recommended pattern is iterative: run structured evaluations, fix failures, and expand coverage as core cases pass.
Include cases that test whether the agent chooses an appropriate tool, supplies valid and relevant arguments, uses returned results correctly, and recovers sensibly when a tool fails or a turn is incomplete. For workflows with multiple calls, evaluate the sequence and final task outcome, not only each isolated call. A response that conforms to a schema can still contain unsupported claims or fail the user’s goal.
Use traces to find where a workflow failed
Google’s agent tutorial describes Cloud Trace spans for LLM calls and tool executions, including latency breakdowns, and documents a path for inspecting content logs. Traces and logs can help locate mismatches between requested and returned shapes, failed calls, and slow steps. Treat them as diagnostic evidence about execution, not as a substitute for evaluations of correctness and task success.
Apply the platform guidance without assuming portability
The OpenAI documentation describes schema-constrained responses and strict function calling; Google’s materials describe JSON API conventions, agent evaluation, and tracing workflows. They address complementary layers, but they do not establish that the platforms accept identical schemas or behave identically. Check current support for the endpoint, model, and schema mode you use. The cited official documentation was reviewed on October 4, 2026; platform behavior may change.
There is no head-to-head benchmark in these materials that supports ranking one platform’s JSON interface as more reliable. Compare concrete requirements instead: schema enforcement and supported features, tool-call and result association, failure behavior, API conventions, evaluation options, and observability.
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