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Patterns for continuous autonomous agent loops with quality gates, evals, and recovery controls.
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Basé sur la classification professionnelle SOC
Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics
Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates.
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
Engineering operating model for teams where AI agents generate a large share of implementation output.
Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code.
Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns.
| name | continuous-agent-loop |
| description | Patterns for continuous autonomous agent loops with quality gates, evals, and recovery controls. |
| source_path | skills/continuous-agent-loop/SKILL.md |
| origin | ECC |
This is the v1.8+ canonical loop skill name. It supersedes autonomous-loops while keeping compatibility for one release.
Start
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+-- Need strict CI/PR control? -- yes --> continuous-pr
|
+-- Need RFC decomposition? -- yes --> rfc-dag
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+-- Need exploratory parallel generation? -- yes --> infinite
|
+-- default --> sequential
Recommended production stack:
ralphinho-rfc-pipeline)plankton-code-quality + /quality-gate)eval-harness)nanoclaw-repl)/harness-audit