How dedicated planning agents, resumable context, and intelligent model routing transform autonomous coding from experimental to production-grade.
When Your AI Agent Needs Its Own Architect
Picture this: Your autonomous coding agent spends three hours refactoring a legacy authentication system. It touches dozens of files, updates import paths, modifies database schemas. You review the pull request and realize it implemented OAuth 1.0a instead of OAuth 2.0 — because somewhere around hour two, it lost track of the original requirements.
I’ve debugged enough of these “almost right” refactors to know the pattern. General-purpose agents drift. They start with clear intent, then get buried in implementation details. By the time they surface, the plan that started in their context window is buried under test failures, dependency conflicts, and exploratory dead-ends.
Claude Code v2.0.28 addresses this with a deceptively simple idea: give the planning phase its own dedicated agent. The implications ripple through everything — from how we structure complex workflows to what becomes possible with multi-agent architectures.
The Core Problem: Context Pollution in Agentic Workflows
Before we examine the solution, let’s understand why autonomous agents struggle with complex, multi-step tasks.
Modern large language models handle single, well-defined problems exceptionally well. Ask them to write a specific function, explain a code pattern, or generate tests for an isolated module — they excel. But chain multiple operations together, and something interesting happens.
The context window becomes a junk drawer.
Planning discussions mix with implementation details. Exploratory code that didn’t work stays in memory. Error messages from failed attempts clutter the conversation. The agent’s attention gets distributed across “what we’re trying to do” (planning) and “what we’re currently doing” (execution).
This manifests in three ways:
Requirement drift: Initial goals blur as implementation progresses. An agent might start refactoring for OAuth 2.0 but slip into OAuth 1.0a because that’s what the existing codebase patterns suggested.
Replanning overhead: When a blocker emerges mid-implementation, the agent must replan while holding all current implementation state in context. This split focus produces suboptimal plans.
Context exhaustion: Long-running tasks fill the context window with artifacts of the journey — code snippets tried and abandoned, debugging output, file contents examined but not modified. Eventually, the original requirements scroll out of the working memory.
What we needed wasn’t smarter general agents. We needed specialized agents with clear boundaries.
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