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74 lines
2.0 KiB
Markdown
74 lines
2.0 KiB
Markdown
---
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name: agent-harness-construction
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description: Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
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origin: ECC
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---
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# Agent Harness Construction
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Use this skill when you are improving how an agent plans, calls tools, recovers from errors, and converges on completion.
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## Core Model
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Agent output quality is constrained by:
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1. Action space quality
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2. Observation quality
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3. Recovery quality
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4. Context budget quality
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## Action Space Design
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1. Use stable, explicit tool names.
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2. Keep inputs schema-first and narrow.
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3. Return deterministic output shapes.
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4. Avoid catch-all tools unless isolation is impossible.
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## Granularity Rules
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- Use micro-tools for high-risk operations (deploy, migration, permissions).
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- Use medium tools for common edit/read/search loops.
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- Use macro-tools only when round-trip overhead is the dominant cost.
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## Observation Design
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Every tool response should include:
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- `status`: success|warning|error
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- `summary`: one-line result
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- `next_actions`: actionable follow-ups
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- `artifacts`: file paths / IDs
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## Error Recovery Contract
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For every error path, include:
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- root cause hint
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- safe retry instruction
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- explicit stop condition
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## Context Budgeting
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1. Keep system prompt minimal and invariant.
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2. Move large guidance into skills loaded on demand.
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3. Prefer references to files over inlining long documents.
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4. Compact at phase boundaries, not arbitrary token thresholds.
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## Architecture Pattern Guidance
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- ReAct: best for exploratory tasks with uncertain path.
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- Function-calling: best for structured deterministic flows.
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- Hybrid (recommended): ReAct planning + typed tool execution.
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## Benchmarking
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Track:
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- completion rate
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- retries per task
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- pass@1 and pass@3
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- cost per successful task
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## Anti-Patterns
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- Too many tools with overlapping semantics.
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- Opaque tool output with no recovery hints.
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- Error-only output without next steps.
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- Context overloading with irrelevant references.
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