# Meta-Agent Paper: Active Management vs Passive Dispatch

## Core Research Question
What makes a good meta-agent? Not just dispatching sub-agents, but actively managing them through contextual communication, proactive intervention, and experience accumulation.

## Key Insight
Current meta-agents (Dobby, ClawTeam, prompted orchestrators) are dispatchers, not managers. They decompose → dispatch → wait → collect. A real manager:
- Gives contextual instructions (not one-size-fits-all prompts)
- Monitors execution and intervenes proactively (not just reacts to failure)
- Learns from past orchestration experience (not stateless each time)

EGAO's effect streams are the feedback channel that enables active management — the meta-agent sees WHAT sub-agents are doing, not just whether they succeeded.

## Three Levels of Meta-Agent Intelligence

### Level 1: Post-hoc Learning (train-free)
- Collect effect streams from completed tasks
- Compress into reusable patterns/skills
- Pack as context for future decisions
- Metric: does experience improve orchestration over time?

### Level 2: Proactive Intervention (train-free)
- Monitor effect stream DURING execution
- Detect anomalies before failure
- Intervene with corrected instructions
- Metric: failure prevention rate

### Level 3: Learned Orchestration Policy (trained)
- Train 8B meta-agent to generate task descriptions + decide when to intervene
- Training signal: outcomes + effect stream quality
- Metric: completion rate, cost efficiency

## Method: EGOL (Effect-Guided Orchestration Learning)

### Train-free version
- Accumulate experience from effect streams as "orchestration skills"
- In-context: pack relevant past experiences when planning new orchestration
- Proactive: monitor live effect streams, intervene when anomalies detected

### Trained version
- 8B model generates NL task descriptions for strong executor (claude -p)
- Weak-to-strong: cheap model orchestrates, expensive model executes
- RL training with GRPO + effect-based per-step credit assignment
- Open-ended NL action space (not fixed tool set like ToolOrchestra)

## Benchmarks

### Existing (evaluate on these):
- SWE-EVO (48 tasks) — multi-step code evolution, 21% current SOTA
- SWE-CI (100 tasks) — long-horizon 71-commit sequences
- Recovery-Bench (NeurIPS 2025) — failure diagnosis and recovery
- RE-Bench (7 tasks, ICML 2025) — research engineering with time budgets
- REALM-Bench — scheduling under disruptions

### New (contribute):
- Meta-Agent Communication Quality — do better instructions → better sub-agent outcomes?
- Proactive Intervention — can meta-agent detect problems before failure?
- Experience Transfer — do meta-agent skills transfer across domains?

## Differentiation from Prior Work

| System | What | Limitation |
|--------|------|-----------|
| ToolOrchestra | RL-trained tool selector | Fixed tool set, single session, scalar reward |
| HyperAgent | Self-modifying agent code | Single agent, no sub-agent management |
| ClawTeam | Agent swarm coordination | Peer-to-peer, no central intelligence, no learning |
| Reflexion | Text-based self-reflection | No structured effects, single session |
| **EGOL** | **Active meta-agent management** | - |

Key differentiator: effect streams enable the meta-agent to be PROACTIVE (intervene during execution) and LEARNING (accumulate orchestration skills), not just REACTIVE (dispatch and wait).

## Baselines for Experiments
1. Single agent (no orchestration)
2. Static planner (decompose once, execute sequentially)
3. ReAct + Reflexion (text-based reflection)
4. ToolOrchestra (RL-trained, fixed tools)
5. ClawTeam (agent swarm)
6. EGOL train-free (experience accumulation + proactive intervention)
7. EGOL trained (8B RL-trained meta-agent)
