select-loop-strategy

Select Loop Strategy: Orchestration Pattern Decision Tree

Provides a deterministic decision framework to help agents and developers select the right execution topology for any given software engineering, research, or system evolution task.

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The Master Decision Tree

Evaluate your task against the following gates in order:

[Incoming Task / Trigger]
    │
    ▼
1. Does the task require strict human approval gates, formal state tracking, 
   transactional worktree isolation, or automatic rollbacks on test failure?
   ├─ YES ──▶ Pattern 7: graph-execution (Deterministic State Machine)
   └─ NO  ──▶ continue
    │
    ▼
2. Can the work be partitioned into 10+ independent, non-overlapping items 
   that execute simultaneously with zero shared state?
   ├─ YES ──▶ Pattern 4: agent-swarm (Parallel Fan-Out)
   └─ NO  ──▶ continue
    │
    ▼
3. Is the primary requirement adversarial critique, security analysis, 
   or multi-perspective red-teaming until an explicit "Approved" verdict?
   ├─ YES ──▶ Pattern 2: red-team-review (Generator / Critic Feedback)
   └─ NO  ──▶ continue
    │
    ▼
4. Does the task involve unguided friction discovery, automated hypothesis 
   testing, and headless benchmark evaluation over long horizons?
   ├─ YES ──▶ Pattern 5: triple-loop-learning (Meta-Learning System)
   └─ NO  ──▶ continue
    │
    ▼
5. Does the task require separating strategy/git management (Outer Loop) 
   from tactical coding/test execution (Inner Loop)?
   ├─ YES ──▶ Pattern 3: dual-loop (Hierarchical Delegation)
   │          (Optionally use co-pilot-loop for Claude + Gemini Flash Low pairing)
   └─ NO  ──▶ continue
    │
    ▼
6. Is this self-directed research, documentation, or local exploratory discovery 
   where the agent works autonomously in a single context window?
   └─ YES ──▶ Pattern 1: learning-loop (Single-Agent Cognitive Continuity)

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Pattern Comparison Matrix

Pattern	Skill	Core Mechanics	Primary Use Case	Risk / Tradeoff
1. Solo Learning	learning-loop	Single context, orientation $\rightarrow$ synthesis $\rightarrow$ closure	Research, documentation, local spikes	Risk of context drift on large tasks
2. Adversarial Review	red-team-review	Generator + multi-persona critics, convergence limit	Security audits, architectural decisions	High token cost; multi-round latency
3. Dual-Loop	dual-loop	Outer Director (Git) $\leftrightarrow$ Inner Worker (No Git)	Features, bugs, bounded code changes	Inner agent must wait for manager review
4. Parallel Swarm	agent-swarm	Partitioned jobs, concurrent batch worker runners	Bulk migrations, mass doc generation	Merge conflicts if tasks share dependencies
5. Meta-Learning	triple-loop-learning	Friction logging $\rightarrow$ hypothesis $\rightarrow$ headless eval	Autonomous system self-optimization	Requires objective automated test harness
6. Fast-Tier Pair	co-pilot-loop	Claude (Director) + Gemini Flash Low (Worker)	Cost-sensitive rapid prototyping	Requires multi-CLI tooling configuration
7. Graph Execution	graph-execution	Deterministic DAG state transitions, receipts, rollbacks	High-assurance self-evolution, safe migrations	Highest structural rigor; state files required