---
title: "Discrete Phase Separation"
summary: "Splits work into separate research, planning, and implementation conversations, each with fresh context, passing only distilled outputs between phases"
status: "emerging"
category: "Orchestration & Control"
url: "https://agentic-patterns.com/patterns/discrete-phase-separation"
source: "https://claude.com/blog/building-companies-with-claude-code"
authors: ["Nikola Balic (@nibzard)"]
tags: ["orchestration","planning","research","context-management","multi-model"]
signals: ["Complex features need significant background research","Refactors where understanding existing code is critical","Mixing research and implementation in one conversation degrades quality"]
anti_signals: ["Small tasks where phase handoffs add more overhead than value","Latency or total token usage must stay low"]
updated_at: "2026-01-05"
---

# Discrete Phase Separation

> Splits work into separate research, planning, and implementation conversations, each with fresh context, passing only distilled outputs between phases

## Use when

- Complex features need significant background research
- Refactors where understanding existing code is critical
- Mixing research and implementation in one conversation degrades quality

## Avoid when

- Small tasks where phase handoffs add more overhead than value
- Latency or total token usage must stay low

## Problem

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.

## Solution

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.

```mermaid
graph LR
    A[Research Phase<br/>Opus 4.1] -->|Distilled Findings| B[Planning Phase<br/>Opus 4.1]
    B -->|Implementation Roadmap| C[Execution Phase<br/>Sonnet 4.5]

    style A fill:#e1f5ff
    style B fill:#fff4e1
    style C fill:#e8f5e9
```

## How to use it

**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

## Trade-offs

**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

## References

- [Building Companies with Claude Code](https://claude.com/blog/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](https://arxiv.org/abs/2403.05441) - Parisien et al., ICLR 2024
- [Design Patterns for Securing LLM Agents against Prompt Injections](https://arxiv.org/abs/2506.08837) - Beurer-Kellner et al., 2025 (Section 3.1: Plan-Then-Execute)
- Related patterns: [Sub-Agent Spawning](sub-agent-spawning.md), [Plan-Then-Execute Pattern](plan-then-execute-pattern.md)