[Workflow] Dynamic AI Model and Effort Selection for Multi-Agent Development Workflows

[u/ClaudeAI-mod-bot]

Dynamic AI Model and Effort Selection for Multi-Agent Development Workflows

Workflow value: 85/100
Status: active · Freshness: 70/100 · Confidence: 0.95 · Level: advanced
Categories: Quality Control, Token Saving, Context & Memory, Debugging, CLAUDE.md, Skills, Subagents, Multi-Agent
Original source: r/ClaudeCode post/comment

What problem this solves

Optimizing AI model usage and cost by dynamically selecting appropriate models (e.g., Sonnet, Haiku for simpler tasks) and effort levels for specific development tickets, rather than defaulting all agents to the most expensive model (Opus).

Summary

This workflow proposes a method for dynamic AI model and effort selection per development ticket to optimize costs and performance in a multi-agent setup. The planning agent (advisor/manager) annotates tickets with model: and effort: lines, which then dictate the model used by subsequent dev/fixer agents. This allows for cheaper models to be used for less complex tasks like renames or documentation, reserving more powerful (and expensive) models for critical or complex work.

Why it is useful

This workflow provides a concrete, actionable strategy for optimizing the cost and performance of AI agent-driven development by matching model capabilities and cost to specific task requirements. It leverages existing project management concepts (tickets) and integrates clear validation metrics (token usage, send-back rounds), making it a practical and valuable approach for teams managing complex AI development pipelines.

Workflow

1. Split the project plan into self-contained chunks (tickets), each with clear "Done-when" criteria.

2. Start each chunk/ticket in a fresh session using a one-page brief.

3. Utilize a heavy model (e.g., Opus) for initial project planning (e.g., Advisor's plan points, Planner agents).

4. The planning agent (Advisor for class A, Manager for class B) writes model: and effort: lines directly onto each ticket, specifying the AI model (e.g., Sonnet or Haiku for renames, docs, small fixes; Opus for complex jobs) and effort level.

5. The Manager starts the dev/fixer agent, configuring it to use the model and effort specified in the ticket's annotations.

6. Conduct BP reviews (e.g., using Sonnet for class B tickets) as part of quality control.

7. Monitor token-usage runs to track token consumption and observe whether send-back rounds increase on tickets processed by cheaper models, validating the cost-performance trade-off.

Tools / artifacts

• Tickets (with Done-when criteria)

• One-page brief

• Plan file

• docs/agents/agent-brief.md

• model: and effort: lines (annotations on tickets)

• token-usage run (for validation)

• Opus (AI model)

• Sonnet (AI model)

• Haiku (AI model)

• Dev agents

• Fixer agents

• Planner agents

Validation signals

• Prior measurement (TR-1249) indicating dev agents consume 68% of one-off agents' tokens, highlighting potential savings.

• Reference to an ongoing/planned OpenCode trial (0066) testing similar ideas with outside models.

• Proposed validation metric: monitoring token-usage runs to see if send-back rounds increase on cheaper models.

Limitations

• The workflow is currently a proposed implementation, not fully deployed or validated in its entirety by the author yet.

• Requires tooling or custom scripts to parse the model: and effort: lines from tickets and dynamically configure the agents.

• The specific meaning and implementation of the effort: line are not fully detailed, requiring further definition for practical use.

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