Anthropic’s MCP Code Execution Revolution.

The world of AI agents is shifting rapidly. Anthropic has introduced one of the most significant architectural pivots in agent design. Their new code-execution-based MCP framework transforms how agents manage context, execute tasks, reuse logic, handle privacy, and scale across enterprise environments.
This blog breaks down everything important, with all source links included.

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What Is MCP and Why It Needed an Upgrade

Anthropic’s earlier version of MCP (Model Context Protocol) required agents to load every tool definition, schema, and intermediate result directly into the model’s prompt window.
This created huge issues.

• Too many tokens
• Slow workflows
• High cost
• Large and messy contexts
• No place for persistent logic or state

Anthropic explains the motivation here:
🔗 https://www.anthropic.com/engineering/code-execution-with-mcp
🔗 https://www.theunwindai.com/p/code-execution-with-mcp-by-anthropic

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The New Architecture. Code Execution Instead of Prompt Bloat

Instead of forcing the model to call tools directly with everything preloaded, Anthropic now lets MCP servers act like code modules.
The agent writes TypeScript, which runs inside a sandboxed execution environment.
This cuts context usage by up to 98%.

How it works:

1. Agent receives the task
2. Agent plans the workflow
3. Agent writes TypeScript that imports only what it needs
4. Sandbox executes the code securely
5. Only small, filtered results return to the model

Key sources:
🔗 https://www.anthropic.com/engineering/code-execution-with-mcp
🔗 https://www.marktechpost.com/2025/11/08/anthropic-turns-mcp-agents-into-code-first-systems-with-code-execution-with-mcp-approach/
🔗 https://www.flowhunt.io/blog/the-end-of-mcp-for-ai-agents-code-execution/

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Skills. The New Brain of Agents

Anthropic also introduced Agent Skills, a major change from stateless prompting to persistent logic.

Agents can now:

• Save reusable workflows
• Store intermediary logic
• Build their own code libraries across tasks
• Improve scripts over time
• Version and refine skills just like software

Sources:
🔗 https://www.anthropic.com/engineering/equipping-agents-for-the-real-world-with-agent-skills
🔗 https://www.linkedin.com/posts/jasonzhoudesign_anthropic-just-released-agent-skills-it-activity-7385224400012451840-_c8F

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Privacy & Security. Sensitive Data Never Reaches the Model

One of the biggest breakthroughs is tokenized sensitive fields.
Sensitive information stays inside the execution environment.
The model only sees placeholders unless you explicitly log information.

This gives enterprises confidence to adopt LLM agents for regulated workflows.

Source:
🔗 https://www.marktechpost.com/2025/11/08/anthropic-turns-mcp-agents-into-code-first-systems-with-code-execution-with-mcp-approach/
🔗 https://www.linkedin.com/posts/hanah-marie-darley_code-execution-with-mcp-building-more-efficient-activity-7392586873124179970-Lxws

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Enterprise Adoption at Scale

Anthropic is forming major partnerships as companies start using agents deeply inside their ecosystems.

One of the most public examples is Cognizant rolling out Claude-based agents to 350,000 employees.
This demonstrates how code-based agents integrate with regulated workflows.

Source:
🔗 https://www.anthropic.com/news/cognizant-partnership

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How Code Execution Replaces Traditional MCP Context Management

Here are the most important changes.

1. On-Demand Loading

Agents dynamically import only the tools and data they need.
No more context stuffing.
🔗 https://www.flowhunt.io/blog/the-end-of-mcp-for-ai-agents-code-execution/

2. Local Data Processing

Intermediate data is handled inside the sandbox.
This improves privacy and reduces token consumption.
🔗 https://www.anthropic.com/engineering/code-execution-with-mcp

3. Real Programming Control Flow

Loops, conditionals, retries, error handling—all done as TypeScript code.
No more hacky prompt-chaining.
🔗 https://www.anthropic.com/engineering/code-execution-with-mcp

4. Massive Reduction in Tokens

Filtered summaries and minimal outputs are sent to the model.
🔗 https://www.linkedin.com/posts/anthropicresearch_code-execution-with-mcp-building-more-efficient-activity-7391612548493545473-6jhy

5. Maintainability and Modularity

Agents now act like mini software programs instead of prompt sequences.
🔗 https://www.linkedin.com/posts/anthropicresearch_code-execution-with-mcp-building-more-efficient-activity-7391612548493545473-6jhy

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Why This Shift Matters for The Future of AI Agents

This redesign brings several major benefits.

• Faster and cheaper workflows
• Lower latency
• Much more complex automation
• Reduced prompting complexity
• Persistent agent memory for logic
• Stronger security and governance
• Enterprise readiness

Anthropic calls this a move from prompt engineering to agent architecture.
🔗 https://www.anthropic.com/engineering/code-execution-with-mcp

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Step-by-Step. How You Can Build Your Own Agent Efficiently

Here is the practical method to build an agent using the new MCP system.

Step 1. Install MCP servers

These expose functions as code APIs.

Step 2. Install the Claude Agent SDK

This is how your agent writes code and coordinates tasks.

Step 3. Give your agent a skills directory

This acts like persistent logic memory.

Step 4. Give the agent tasks

It will automatically write and execute TypeScript.

Step 5. Let it improve its own skills

Every task becomes part of the agent’s evolving brain.

Step 6. Connect enterprise tools

Databases
CRMs
Documentation
APIs
Internal systems

Agents can orchestrate full business workflows.

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The Beginning of Real Agent Intelligence

Anthropic’s code-execution-based MCP turns agents into true autonomous workers.
They can think.
Plan.
Write code.
Reuse logic.
Handle sensitive data privately.
And scale across organizations.

This is not an LLM with prompts.
This is software powered by reasoning.