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Module 7 of 7 · 56 min

Migrate OpenAI Workflows with Evidence

Inventory dependencies, separate portable contracts from OpenAI-specific behavior, compare APIs or providers, canary safely, and preserve rollback through migration and deprecation.

OpenAI

By the end

You will be able to

  • Inventory API, model, prompt, item, tool, state, data, evaluation, and operational dependencies.
  • Separate portable application contracts from OpenAI-specific capabilities and behavior.
  • Use official migration and lifecycle evidence plus controlled evaluation instead of assuming equivalence.
  • Migrate with coexistence, canary gates, data reconciliation, rollback, and accountable closeout.
01

Inventory before changing

Find every API endpoint, SDK, credential path, project permission, model reference, request parameter, instruction, message or item parser, state link, tool schema, function-result protocol, structured-output contract, data control, evaluation, dashboard, alert, runbook, and cost assumption.

Include ChatGPT or Codex workflows, Responses and legacy API use, hosted tools, batches, caches, stored objects, cloud integrations, and shadow clients. Give each dependency an owner, source, current version, risk, and verification method.

02

Separate portable and OpenAI-specific contracts

User outcomes, application schemas, authorization policy, assessment rubrics, telemetry fields, postconditions, and incident rules can often remain portable. Responses items, previous_response_id, hosted tools, function-call protocols, model controls, safety fields, and product integrations may be OpenAI-specific.

Create an adapter boundary without pretending endpoints, models, or providers are identical. Preserve useful native capability when it matters and expose unsupported behavior explicitly rather than silently weakening safety, quality, or evidence.

03

Use current migration and lifecycle evidence

For a Chat Completions-to-Responses change, compare request shape, instructions, typed Items, output extraction, tool calls and results, conversation state, storage, streaming events, errors, and usage. Read the current official guide for the exact source and target instead of relying on a copied conversion snippet.

For a model or provider change, verify current model guidance, supported features, snapshots or aliases, limits, and lifecycle status. Track deprecation dates and replacements from the official deprecation page and assign owners before urgency removes the ability to evaluate safely.

04

Evaluate, shadow, and canary

Run baseline and candidate against identical representative, edge, adversarial, incident, and holdout cases. Compare quality, refusals, safety, structured output, state behavior, tool selection and arguments, side effects, latency, usage, and cost by critical slice.

Use offline replay, shadow traffic, or a small canary only where data policy and operational risk allow it. Define promotion, hold, and rollback thresholds first; isolate candidate telemetry by version and reconcile possible side effects before shifting more traffic.

05

Migrate reversibly and close deliberately

Use configurable routing, versioned prompts and adapters, backward-compatible record changes, dual-read or dual-run patterns when justified, and a rehearsed rollback. Keep old and new state mappings explicit and avoid irreversible cleanup until the observation window passes.

Close only after production gates pass, rollback time and data reconciliation are proven, security and privacy review is complete, documentation and runbooks are current, deprecated use is absent or accepted with an owner, and stakeholders acknowledge residual risk. Migration away from OpenAI follows the same evidence discipline.

Practice activity

Plan and rehearse an OpenAI migration

  1. Inventory one workflow across API, model, SDK, credentials, instructions, messages or Items, state, tools, data controls, evaluations, telemetry, operations, and product integrations.
  2. Create a portable-versus-OpenAI-specific contract table and map every incompatibility to an adapter, redesign, explicit non-support decision, data migration, or operational change.
  3. Run a baseline/candidate evaluation and write offline, shadow or canary promotion, hold, rollback, data-reconciliation, and deprecation-exit criteria.
  4. Rehearse a synthetic failed canary and record detection, decision, rollback time, state and side-effect reconciliation, communications, owners, and closeout evidence.

What to produce

  • A dependency inventory and compatibility matrix linked to current official migration, model, lifecycle, and production sources for the exact change.
  • A baseline/candidate report, gated rollout plan, successful rollback and reconciliation rehearsal, and signed closeout checklist.

Reflect before continuing

Which dependency looked portable until evaluation exposed an OpenAI-specific state, tool, safety, or data-handling contract?

Evidence

Sources and verification

Knowledge check

Make it stick.

Pass at 80%

Choose the strongest answer for each question. Your attempts become part of your device-local transcript.

01What should happen before changing an API or model reference?
02Which contract is usually most portable?
03What should a Chat Completions-to-Responses migration compare?
04When should canary rollback thresholds be defined?
05What proves migration closeout?