Discrete Phase Separation

When AI agents attempt to simultaneously research, plan, and implement solutions, context contamination occurs. Competing priorities within a single conversation degrade output quality as the agent struggles to balance exploration, strategic thinking, and execution. This results in incomplete research, unclear plans, and suboptimal implementations.

Break development workflows into isolated phases with clean handoffs between them. Each phase runs in a separate conversation with a fresh context window, focusing exclusively on its objective:

Research Phase (Opus 4.1):

• Deep exploration of requirements, existing code, and constraints
• Comprehensive background investigation
• No implementation concerns

Planning Phase (Opus 4.1):

• Create structured implementation roadmap
• Define clear steps and dependencies
• No coding distractions

Implementation Phase (Sonnet 4.5):

• Execute each plan step systematically
• Focus purely on code quality and functionality
• Leverage the distilled outputs from previous phases

Key principle: Pass only distilled conclusions between phases, not full conversation history. This prevents context pollution while maintaining necessary information flow.

When to apply:

• Complex features requiring significant background research
• Refactoring projects where understanding existing code is critical
• New codebases where architectural decisions need careful consideration
• Any task where mixing research and implementation degrades quality

Implementation approach:

1. Research phase - Start fresh conversation with Opus 4.1:

• "Research the authentication system and document all OAuth flows"
  • Compile findings into a structured document
  • Close conversation

2. Planning phase - New conversation with Opus 4.1:

• Provide distilled research findings (not full transcript)
  • "Create implementation plan for adding Google OAuth support"
  • Generate step-by-step roadmap
  • Close conversation

3. Execution phase - New conversation with Sonnet 4.5:

• Provide the implementation plan
  • "Implement step 1: Create OAuth configuration module"
  • Execute systematically through each step

Prerequisites:

• Clear handoff documents between phases
• Discipline to resist combining phases
• Understanding of which model strengths to leverage

Pros:

• Higher quality outputs in each phase due to focused attention
• Prevents context contamination from competing objectives
• Deliberation before action improves tool use accuracy from 72% to 94% (Parisien et al. 2024)
• Leverages model-specific strengths (Opus for reasoning, Sonnet for execution)
• Clearer mental model for complex projects
• Easier to debug which phase introduced issues

Cons:

• Requires more explicit phase management and handoffs
• Planning overhead adds ~35% latency (Parisien et al. 2024)
• Requires discipline to maintain phase boundaries
• Information loss risk if handoffs are poorly structured
• Higher total token usage across multiple conversations

• Building Companies with Claude Code - Sam Stettner (Ambral) emphasizes: "Don't make Claude do research while it's trying to plan, while it's trying to implement."
• Deliberation Before Action: Language Models with Tool Use - Parisien et al., ICLR 2024
• Design Patterns for Securing LLM Agents against Prompt Injections - Beurer-Kellner et al., 2025 (Section 3.1: Plan-Then-Execute)
• Related patterns: Sub-Agent Spawning, Plan-Then-Execute Pattern