t1094.1: Update build-agent to reference pattern data for model tier recommendations#1633
t1094.1: Update build-agent to reference pattern data for model tier recommendations#1633marcusquinn merged 2 commits intomainfrom
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…ommendations (t1094.1) Add evidence-based model routing guidance to build-agent.md: - New 'Model Tier Selection: Evidence-Based Routing' section with pattern-tracker-helper.sh examples - Quick Reference update: prompt to check pattern data before setting model: in frontmatter - Shows how pattern data (>75% success, 3+ samples) overrides static routing rules - Concrete examples: 'pattern data shows sonnet with prompt-repeat is optimal for shell-script agents' - Documents how to record outcomes to build pattern history over time - Explains why evidence beats rules: self-correcting as patterns accumulate
Summary of ChangesHello @marcusquinn, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request updates the agent design guide to incorporate evidence-based model tier recommendations. It moves beyond static rules by integrating pattern data derived from actual task outcomes, enabling agents to make more informed and adaptive decisions about which language model to use. The changes aim to improve the reliability and effectiveness of agent operations by leveraging empirical evidence for model selection. Highlights
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Code Review
This pull request significantly improves the build-agent.md documentation by introducing an evidence-based approach for model tier selection, moving away from purely static rules. The new sections are well-written and provide helpful examples using pattern-tracker-helper.sh. My review includes a couple of suggestions to enhance clarity and consistency within the new documentation, specifically regarding the format of task-type arguments and the wording in the decision table. These changes will help ensure users can apply the new data-driven methodology correctly.
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Actionable comments posted: 2
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In @.agents/tools/build-agent/build-agent.md:
- Around line 425-437: The --task-type value is inconsistent between the suggest
call ("shell script agent") and the record call ("shell-script-agent"), which
can break lookups; update usages so the same canonical format is used everywhere
(pick one form, e.g., hyphenated) or ensure normalization inside
pattern-tracker-helper.sh; specifically change the call site that invokes
suggest to use the same --task-type token as record (or add normalization logic
in pattern-tracker-helper.sh to canonicalize --task-type inputs), and verify
places referencing pattern-tracker-helper.sh, suggest, and record all use the
chosen canonical token.
- Around line 441-450: The frontmatter uses a bare model alias "model: sonnet"
which violates OpenCode's required provider/model-id format; update the YAML
frontmatter key `model` to a full model identifier (for example
`anthropic/claude-sonnet-4-20250514`) or to whatever canonical provider/model-id
your project uses so the resolver can find the model (edit the `model:` line in
the block shown to replace `sonnet` with the full provider/model-id).
marcusquinn
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…d t1094.1 (t1107) All t1094 deliverables confirmed implemented: - Prompt strategy tracking → t1095 (pr:#1629) - Output quality gradient + failure categorization → t1096 (pr:#1632) - Token usage → t1095 (pr:#1629) - A/B comparison → t1098+t1099 (pr:#1637, pr:#1634) - Prompt-repeat strategy → t1097 (pr:#1631) - Build-agent reference → t1094.1 (pr:#1633) t1094 parent ready to be marked complete: verified:2026-02-18
1 participant
Update build-agent.md to use evidence-based model tier recommendations via pattern data, not just static rules.
Changes
build-agent.mdmodel:in frontmatterpattern-tracker-helper.sh suggest/recommendprovides data-driven tier selectionMotivation
The agent design guide previously only referenced static routing rules ("use opus for architecture"). This update adds the missing link to pattern data — empirical evidence from actual task outcomes — so agents can make data-driven model tier decisions rather than rule-based guesses.
Ref #1625
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