AI trading schema migrations should be tested as contracts between specific producer and consumer versions. Identify every pair that can meet during rollout, replay preserved payloads through each pair, and verify that accepted messages retain their original meaning. Use explicit adapters for supported changes and reject unsupported versions or lossy conversions. Passing a new schema alone does not establish compatibility with queued messages, older services or approved trade proposals.
A research agent can produce a perfectly valid version-two proposal while the order service still understands version one. The resulting failure may be a clear rejection, or a quieter change in meaning if a field keeps its name while its units change. A migration plan needs to explain both outcomes before either release reaches an execution path.
This is an engineering worksheet for evaluating a hypothetical trading workflow. It does not describe an available KLINEO schema migration feature.
Define compatibility in both directions
The producer creates the message: perhaps an AI analysis node, a tool adapter or a saved-workflow loader. The consumer reads it: perhaps a risk service, approval screen, journal or execution adapter. Inventory all of them. An order service accepting the change does not prove the approval screen displays it correctly.
Backward compatibility means a newer consumer can read older producer data. Forward compatibility means an older consumer can read newer producer data. Confluent's schema evolution documentation distinguishes those directions and checks across adjacent versions from transitive checks across earlier versions. Its implementation rules vary by format and configuration; they are not universal JSON migration guarantees.
Write the actual requirement in plain language: “Consumer C2 must interpret every retained version-one proposal,” for example. If C1 will never receive version-two data, enforce that routing boundary instead of claiming forward compatibility.
The structured-output validation guide covers whether one proposal fits its selected contract and independent trading rules. Migration adds a different question: which releases can exchange that proposal without changing its meaning?
Freeze the meaning before changing the field
Preserve the old schema, representative payloads and consumer behavior. Record field units, missing-value behavior, enumeration meanings and rounding rules, alongside the application contract version and validator release. A version label identifies a contract only when its definition is retained.
Keep these contract identities separate from the model, prompt and tool releases in the model version-pinning guide. An unchanged model can emit a newly requested format.
Consider a fictional spot proposal contract. In version one, max_slippage_fraction is a decimal string representing a fraction. In version two, max_slippage_bps is an integer number of basis points. Both express a permitted adverse price deviation; they do not predict or guarantee a fill. The instrument, side and base quantity remain unchanged.
For this constructed example, the proposal is 0.004 BTC at a 60,000 USDT reference price. Its reference notional is 0.004 × 60,000 = 240 USDT. A version-one cap of "0.005" means 0.5%, or 50 basis points. Version two must therefore express it as 50, not 0.005 or 5. These are teaching values, not suggested settings or current exchange rules.
| Preserved input | Exact conversion | Version-two result |
|---|---|---|
Fraction "0.005" | 0.005 × 10,000 = 50 bps | Integer 50; meaning preserved |
Fraction "0" | 0 × 10,000 = 0 bps | Integer 0; retain the zero-cap meaning |
Fraction "0.000025" | 0.000025 × 10,000 = 0.25 bps | Reject this migration: integer bps cannot represent it exactly |
| Fraction missing | No value exists to convert | Reject; do not invent a cap |
The last two rows expose migration policy. Rounding 0.25 bps up to 1 bps makes the allowed deviation four times larger; rounding down changes it to zero. Either choice changes the original instruction. A revised contract could support fractional basis points, but that is another explicit change requiring tests.
Use decimal arithmetic for the adapter and test the accepted precision. Do not make a model infer units from the value's apparent size. Likewise, do not reinterpret an old field in place because its JSON type still passes validation.
Test the four release pairs
Assume P1 emits version one, P2 emits version two, C1 accepts only version one, and C2 deliberately supports both through an explicit version-one adapter. The following are expected outcomes for the exact 50-bps fixture, not reported software test results.
| Producer → consumer | Required result | Meaning check |
|---|---|---|
| P1 → C1 | Accept the original contract | Effective cap remains 50 bps |
| P1 → C2 | Validate v1, adapt, then validate v2 | Effective cap remains 50 bps |
| P2 → C1 | Reject unsupported contract; no submission | New fields never fall back to old defaults |
| P2 → C2 | Accept the native v2 contract | Effective cap remains 50 bps |
Acceptance here concerns contract interpretation. It does not waive freshness, balance, risk or authorization checks. Run the matrix with a mocked execution boundary and assert that every rejected fixture produces zero submission calls.
For each supported route, compare the normalized instrument, side, quantity, price constraints and decision branch, rather than merely checking that deserialization succeeded. Include hold, empty optional text, explicit null where permitted, unknown enumeration values and malformed version labels.
An “optional” addition can still break an old reader. A JSON Schema object using additionalProperties: false rejects an unrecognized field; a property declaration alone does not make a field required. The JSON Schema object reference explains those rules. Test the real old validator with the new payload, including the version discriminator, instead of assuming that an additive change is harmless.
Another trap is using default to patch an absent migration input. JSON Schema annotation documentation states that validation does not fill missing values from default. An application may implement defaults separately, so preserve and test that application behavior too. An unspecified slippage cap must not become unlimited permission through a fallback.
Deploy the reader before the new writer
A practical rollout for this example has five steps:
- Preserve the v1 schema and fixtures, then release C2 with explicit support for v1 and v2. Keep P1 active while checking its unchanged interpretation.
- Route unknown contracts to a recorded rejection state. Do not guess the version from whichever fields happen to be present.
- Enable P2 for a bounded test cohort and compare normalized decisions, rejection reasons and displayed approval details with expectations.
- Monitor traffic by producer version, consumer version and adapter version. Retain failed payloads with sensitive fields excluded or appropriately protected.
- Retire v1 only after checking active producers, queued messages, retries, stored proposals and the required historical reading window.
These are design recommendations, not a provider guarantee. Attach the release manifest to the run so the deployed validator and adapter cannot silently differ from the reviewed ones. The configuration drift guide examines that separate problem.
A queue can outlive a deployment. Even after P1 stops, an old proposal may arrive through delayed delivery or recovery. Test the oldest retained version that can actually reappear. Testing only v1 against v2 is insufficient if retained v0 messages remain eligible for consumption.
Roll back the complete contract path
Reverting C2 to C1 while P2 continues writing recreates the incompatible pair. Define rollback as a supported producer, consumer, adapter and routing combination. Stop or reroute new-version production before removing the only reader that understands it, and reconcile any already-submitted orders independently.
Do not overwrite historical payloads to make the rollback look clean. Preserve the original message, transformation record and normalized result. A migrated representation also should not silently inherit approval bound to different bytes or a different contract version. Check the authorization rule and obtain any required approval for the interpreted action; the human approval guide explains why the exact plan matters.
Finally, retain the fixture bundle, schema bytes, adapter release, clock assumptions and observed actions. The deterministic replay guide covers freezing a complete historical run. A migration test can establish that specified examples preserve their meaning; it cannot establish profitable decisions or guarantee compatibility for untested consumers.
Prepared with AI assistance under the editorial policy. The useful release evidence is a supported-pair matrix with explicit rejection behavior and a rollback path that can still read the messages already in flight.