A new architectural pattern is emerging in enterprise AI: use Claude in the cloud to design and plan complex tasks, then hand execution off to a local AI model running on your own servers. This hybrid approach, called the planner-executor pattern, solves a problem that has been nagging IT teams since generative AI went mainstream: cloud models like Claude are powerful but expensive, while local models are cheap but not smart enough to handle complex work alone.

What Is the Planner-Executor Pattern and Why Does It Matter?

The pattern works by splitting responsibilities between two AI systems. Claude, running in Anthropic's cloud, acts as the "senior engineer" who reads requirements, asks clarifying questions, and writes a detailed step-by-step plan. A local AI model, such as Qwen, DeepSeek, or Llama running on an office server, becomes the "operations engineer" who executes that plan without needing to invent the design.

The financial impact is substantial. In a typical coding refactor task, Claude alone would burn roughly 150,000 tokens doing all the work. With the hybrid approach, Claude uses only about 10,000 tokens for planning and verification, while the local model handles the token-heavy execution phase. For organizations running AI across teams of 10 or more developers, this translates to dramatically lower API bills.

Beyond cost, the pattern addresses a critical privacy concern: your source code never leaves the organization. Only the high-level plan and final code diff travel to Claude's servers, not the full codebase.

How Does the Four-Stage Workflow Actually Function?

The planner-executor pattern follows a repeatable four-stage cycle:

• Intake: Claude reads the initial request and asks clarifying questions to ensure the requirement is clear before proceeding.
• Planning: Claude decomposes the task into sub-tasks, names specific files to modify, and lists edge cases in a structured JSON or markdown format.
• Execution: The local AI model reads the plan, accesses files, edits code, and runs tests using its own tool-use capabilities, then returns a code diff and execution log.
• Verification: Claude reviews the diff and log against the original plan, approves the work, or sends it back for revisions if something does not match.

Stages 1, 2, and 4 use Claude, but they consume far fewer tokens than stage 3 because thinking and reviewing require fewer characters than actually implementing code. The most token-intensive stage moves to the local model, which costs only electricity and hardware.

Which Local Models Can Actually Serve as Executors?

Not every open-source model is strong enough to execute complex plans. The executor must excel at instruction following and tool use, or the pattern breaks down mid-task. As of May 2026, only a handful of models genuinely work in this role:

• Qwen2.5-Coder-7B: Suitable only for small tasks like autocomplete; instruction following is too weak for multi-step execution.
• Qwen2.5-Coder-14B: Can handle single-file refactors and represents the minimum viable executor for this pattern.
• Qwen2.5-Coder-32B: The sweet spot for most enterprises, capable of multi-file work and tool use without requiring massive hardware.
• Llama 3.3 70B: A general-purpose executor with exceptionally strong instruction following, suitable for complex enterprise workloads.
• DeepSeek-V3: A 671-billion parameter mixture-of-experts model requiring multi-GPU clusters, designed for the largest enterprise deployments.

Hardware determines which model you can run. An RTX 5090 GPU can comfortably run Qwen2.5-Coder-32B, making it the practical starting point for teams of 3 to 5 developers. Larger organizations deploying DeepSeek-V3 need H100 or B200 GPU clusters.

What Tools and Frameworks Support This Pattern?

Several frameworks now support planner-executor architecture out of the box, eliminating the need to build the orchestrator from scratch:

• Claude Agent SDK: Allows teams to build agents that use Claude as the planner and delegate to sub-agents running different models for execution.
• Ollama: Runs the local AI model and exposes an OpenAI-compatible API that the orchestrator can call, making integration straightforward.
• MCP (Model Context Protocol): A common protocol that both Claude and local AI use to talk to the same tools and data sources, enabling seamless integration.
• LangGraph: A graph-based orchestrator that makes the planner-to-executor-to-verifier flow explicit and supports resumable runs for complex workflows.

A practical starting stack for most enterprises combines Claude Agent SDK as the planner, Ollama hosting Qwen2.5-Coder-32B as the executor, and MCP servers to give both layers access to the same tools and context.

How to Implement the Planner-Executor Pattern in Your Organization

• Assess your hardware: Determine whether you have an RTX 5090, A6000, or larger GPU available. This decision dictates which executor model you can run and how many concurrent users the system can support.
• Choose your executor model: Start with Qwen2.5-Coder-32B if you have an RTX 5090 or equivalent. If you need stronger instruction following or have larger hardware, consider Llama 3.3 70B or DeepSeek-V3.
• Set up Ollama or vLLM: Install your chosen framework to run the local model and expose an API. Ollama is simpler for smaller deployments; vLLM offers higher throughput for production multi-user serving.
• Configure Claude Agent SDK: Build the planner layer using Claude Agent SDK, which handles the intake, planning, and verification stages automatically.
• Connect via MCP: Use Model Context Protocol to give both Claude and your local model access to the same file systems, tools, and data sources, ensuring consistent context.
• Test with a pilot task: Start with a single-file refactor or code generation task to validate the workflow before rolling out to your development team.

What Are the Real Trade-Offs of This Approach?

The planner-executor pattern solves cost and privacy, but it introduces new challenges that real teams encounter in production. The local executor may read Claude's plan incompletely, follow it halfway, then improvise, especially if the model is not strong enough. The fix is to verify every batch and retry whenever the code diff does not match the plan.

Latency is another trade-off. A task that takes 10 seconds with cloud-only Claude might take 30 to 60 seconds in hybrid mode because you are waiting on two network legs plus local AI inference. This makes the pattern a poor fit for real-time user interfaces but an excellent fit for batch jobs and background tasks.

Context window limitations also matter. Claude supports 200,000 to 1 million tokens of context, but most local AI models max out at 32,000 to 128,000 tokens. If the plan is long and the executor also needs to read large code files, important context can get cut off, causing execution failures.

Why Are Developers Choosing Ollama Over Other Local AI Tools?

When comparing local AI tools, Ollama stands out for a different reason than its competitors. While Jan.ai and LM Studio offer desktop chatbot interfaces that feel like ChatGPT, Ollama is intentionally designed as an API-first tool rather than a chatbot app.

This distinction matters for enterprise use. Ollama exposes a local API that other applications and services can call, enabling developers to integrate local language models into automation workflows, voice assistants, and backend systems without sending data to the cloud. For example, a developer can run a local LLM in Ollama and use it as a conversation agent in Home Assistant, giving a voice assistant AI-powered abilities while keeping all data local.

The trade-off is simplicity. Ollama's desktop app is minimal, with just two menu options: New Chat and Launch. If you want a straightforward chatbot experience on modest hardware, Jan.ai is the better choice. But if you need API access to a local model for integration into larger systems, Ollama is the tool developers are choosing.

What Does the Broader AI Infrastructure Landscape Look Like in May 2026?

The planner-executor pattern reflects a larger shift in how enterprises are building AI systems. Open-weight models from DeepSeek, Mistral, and the Llama family continue to improve and are increasingly competitive on benchmarks, encouraging broader adoption for fine-tuning and edge deployment.

Tooling for building agents and developer toolkits remains prominent, with community projects highlighting open agent frameworks and low-code environments for agent creation. Microsoft Research released Webwright, a terminal-native web agent framework that improved benchmark performance from 33.5% to 60.1% by focusing on structured planning and tool orchestration.

Tencent open-sourced TencentDB Agent Memory, a four-tier local memory pipeline built for AI agents and autonomous workflows, supporting hierarchical memory management and persistent context storage to improve long-term reasoning. These infrastructure advances make it increasingly practical for enterprises to build and deploy hybrid AI systems that combine cloud and local models.