agentsys
A modular runtime + orchestration system for AI coding agents — 24 plugins, 49 agents, 44 agent-skills — for Claude Code, OpenCode, Codex CLI, Cursor, and Kiro. The clearest statement of the agentic-coding-harness thesis: frontier models can write code; the open problem is “everything else” — task selection, branch management, review, CI/CD, deployment.
Architecture principles
- Separation of concerns — each agent has a single responsibility and a specific model assignment (cheaper models where they suffice).
- Tool-based efficiency — deterministic work (static analysis, regex, AST parsing) is done with tools, not LLM tokens.
- Certainty grading — findings tagged HIGH/MEDIUM/LOW confidence to route auto-fix vs. manual review (the same honesty/uncertainty discipline claude-opus-4-8 foregrounds).
- Phase gates — pipelines enforce ordered steps so agents can’t skip testing/review; the execution-time sibling of spec-driven-development.
Capabilities
Slash commands: /next-task (end-to-end workflow), /prepare-delivery (quality gates),
/deslop (strip AI artifacts), /audit-project (multi-agent review). State persists across
sessions so interrupted work resumes. Claimed benchmark: Sonnet + agentsys beats raw Opus on
cost-effectiveness by leaning on structure + repo intelligence rather than raw model power.
Why it belongs here
A direct instance of agentic-coding-harness and agent-orchestration, and a sharp data point for the model-substrate thread: a good harness can make a cheaper model outperform a stronger one — structure as a substitute for capability. (Caveat: self-reported benchmark.)
Related
agentic-coding-harness · agent-orchestration · agent-skills · spec-driven-development · agent-kanban · claude-opus-4-8