implementation

Mastra Workflow Configuration

Inspect the original prompt language first, then copy or adapt it once you know how it fits your workflow.

Linked challenge: Sovereign AI Inference Routing with Mastra AI and Ray Serve

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Linked challenge

Sovereign AI Inference Routing with Mastra AI and Ray Serve

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Original prompt text with formatting preserved for inspection.

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Define a Mastra AI workflow in TypeScript that uses the 'Workflow' class to create a conditional step. If the 'primary' step (calling GPT-5.4 Pro) fails or takes over 2 seconds, execute a 'fallback' step using Claude Sonnet 4.6.6.

Adaptation plan

Keep the source stable, then change the prompt in a predictable order so the next run is easier to evaluate.

Keep stable

Hold the task contract and output shape stable so generated implementations remain comparable.

Tune next

Update libraries, interfaces, and environment assumptions to match the stack you actually run.

Verify after

Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.

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implementation

This prompt already mixes executable detail with instructions, so tune examples and interfaces before rewriting the scaffold.

Linked challenge

Sovereign AI Inference Routing with Mastra AI and Ray Serve

In response to the UK's Sovereign AI fund and the Callosum initiative for chip-agnostic AI, this challenge focuses on building a high-performance inference orchestrator. You will use Mastra AI (TypeScript) to create a workflow that dynamically routes requests between GPT-5.4 Pro and Claude Sonnet 4.6.6 based on latency and cost metrics. The system will be deployed using Ray Serve and Cerebrium to manage distributed inference across different hardware profiles. You will implement a custom Mastra workflow that performs model-switching and handles failovers without losing agent context, simulating a sovereign cloud environment that optimizes for domestic resource availability.

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