routr-agents

name	routr-agents
description	Design multi-agent systems, agent loops, and eval harnesses. Use when: building agents, agent operating loop, research/self-correction loop, harness, memory, tools, context engineering, eval loop. Not for: a single AI SDK chat/RAG feature (→ routr-ai).

routr-agents

Goal: principled agent architecture — not ad-hoc prompt stacking.

When to activate

• Building agents, multi-agent systems, agent operating/research loops
• Eval harness, context engineering, memory, tool/MCP design

Do not activate

• Single AI SDK chat/RAG feature, no architecture question → routr-ai
• Agent UI (chat window, controls) → routr-frontend

Iron law

Prototype the harness on one path before scaling prompts — never scale a pattern that hasn't been proven once.

0. Bootstrap

See routr-catalog/references/skill-registry.md. Load by sub-task only — never read the whole cluster.

Context engineering

Sub-task	Skill
Concepts	context-fundamentals
Degrading mid-session	context-degradation
Long handoff	context-compression
Token budget	context-optimization
File-based context	filesystem-context
Memory	memory-systems
Tools / MCP	tool-design

Loop engineering (the operating loop — growing fast, pick per symptom)

Sub-task	Skill
Design the agent operating / research loop	harness-engineering
Robust research loop (validation, error recovery)	comprehensive-research-agent
Debug a misbehaving loop (drift, tool confusion, repeat failures)	reasoning-trace-optimizer
Eval loop / quality gate	evaluation, advanced-evaluation
Multiple agents / handoffs	multi-agent-patterns
Subagent delegation loop	subagent-driven-development
Background / hosted / self-spawning loop	hosted-agents
Cross-agent memory briefing	latent-briefing

Runtime: lean-ctx when implementing.

Budget gate: read one row per active symptom. Do not preload the context + loop clusters together.

Skip: frontend-design, caveman unless terse docs requested.

1. Clarify

Single vs multi-agent; human-in-loop; context budget; eval criteria.

2. Design

Pattern → tools → memory → harness.

3. Implement

Prototype harness before scaling prompts.

Output format

## Agent design report
**Pattern:** single / multi-agent, loop shape
**Context, memory, tools:** …
**Prototype status:** proven on one path / not yet

Handoff

• Wrong file reads → routr-debug + symdex
• Agent UI → routr-frontend

References

• boundaries