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Build a Model Ecosystem

Build a Model Ecosystem

Learn how to combine different AI models and tools for efficient workflows.

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AI models each have their strengths:

  • Some excel at deep reasoning.

  • Some are fast at generating text.

  • Others handle structured coding tasks or multimodal input. 

Treating them interchangeably leads to fragile workflows, wasted tokens, and inconsistent results.

Staff+ engineers design AI systems that solve problems at scale. And you can’t do that by relying on one model for everything. Instead, you should build model ecosystems, where each model does what it’s best at.

A model ecosystem

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A well-architected model ecosystem routes tasks to the right model based on complexity, scope, and cost.

Here’s how this looks in practice:

  • Use reasoning-optimized models for System Design, migration planning, or debugging ambiguous failures.

    • Traits: Slower and more expensive, but exceptional at planning and reasoning.

    • Example: GPT-5 Thinking or Claude 4.1 Opus

  • Use mid-tier models for mechanical, scoped tasks, like refactoring, boilerplate generation, and test scaffolding.

    • Traits: Fast, cheaper per token, and accurate when scoping the problem.

    • Example: GPT-5 or Claude 4.5 Sonnet

Think of it like designing a team structure:

  • Architects (reasoning models) map out the plan.

  • Builders (mid-tier models) execute scoped tasks at scale.

  • Reviewers (you + CI) audit diffs and outputs.

Coding agents and AI IDEs

Many engineers open Claude Code, Codex, or Gemini CLI in one terminal tab while running Windsurf or Cursor in another. They bounce back and forth, half-using each other, never really committing. The result? Overlapping tools and duplicated effort.

Instead, compose them into a pipeline:

  • Use heavy reasoning agents (Claude Code, Gemini CLI, Codex) for repo analysis, planning, and spec writing. 

  • Use AI IDEs (Cursor, Windsurf) for controlled, incremental execution with strong diff visibility. 

If you’re working with large codebases, treat agents as system architects and IDEs as developer teams—you preserve intent while scaling execution.

Here are some use cases to think about for different tools:

  • Claude Code with Opus: This is excellent for repo analysis, migration planning, and producing structured specs. Its UX is a little clunky, but it shines in “architect” mode.

  • Cursor: It is the smoothest for incremental execution. You feed it spec items one at a time, and it applies changes with diffs inline. It’s perfect for iterative building.

  • Windsurf (integrated terminal): It’s a strong tool for exploratory work and prototyping, especially CLI-based workflows.

Make it repeatable

If you’re serious about leveling up, treat model selection and tool orchestration like any other engineering problem: prototype, benchmark, refine.

Prototype → benchmark → refine:

  1. Pick a representative task (e.g., split utils.py into modules + tests).

  2. Plan with a reasoning model (artifacts: spec, risks, file plan).

  3. Execute in Cursor/Windsurf with small PRs and CI.

  4. Track tokens, cost, wall-time, and revert rate; adjust routing.

John would perfect his own pipeline and keep it to himself. You’ll leave a pipeline the team can run on Monday.

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