How Claude Code Is Raising the Ceiling of Intelligence

Anthropic’s Claude Code platform is evolving from a powerful coding assistant into a fully customizable agent framework. With new capabilities now in play and allow to extend thinking, tool use, MCP integration, persistent memory, context editing, secure code execution, and agent skills, all of which demonstrate how Claude Code is raising the ceiling on what intelligent systems can do.

Claude Code’s new capabilities raise the ceiling of agent intelligence through:

• Extended thinking → deeper reasoning

• Tool use → interacting with systems

• MCP → contextual awareness

• Memory → long-term coherence

• Context editing → efficient state management

• Code execution → safe real compute

• Agent skills → expertise and autonomy

Together, they form a flexible, powerful agent platform that combines reasoning, tools, context, and skills, making up the foundational ingredients of truly intelligent systems. Below is a guided tour of these features, based on Anthropic’s presentation given at the AI Engineer Code event in New York in November 2025.

Extended thinking

Extended thinking allows developers to explicitly allocate deliberation budget to Claude. This means you can control the depth, rigor, and “chain-of-thought bandwidth” Claude uses when solving difficult problems.

response = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=8192,
    thinking={
        "enabled": True,
        "budget_tokens": 20000  # You control the depth
    },
    messages=[{
        "role": "user",
        "content": "Help me debug why this system fails under load"
    }]
)

With thinking.enabled=True and a configurable budget_tokens, you can:

• Allow Claude to explore hypotheses deeply.
  Improve debugging quality by letting the model examine more internal steps.
  Tune intelligence vs. cost depending on your use case.

This essentially gives developers a “deliberation dial,” empowering agents to handle more complex reasoning workloads.

Tool use

Claude Code now supports custom tools with schema-defined inputs. This lets your agent interact with external systems, from running tests to modifying files in a secure, typed, predictable way:

tools=[
    {"type": "bash"},
    {"type": "text_editor"},
    {
        "name": "run_tests",
        "description": "Run the project's test suite and return results",
        "input_schema": {
            "type": "object",
            "properties": {
                "test_path": {"type": "string", "description": "Specific test directory"},
                "verbose": {"type": "boolean", "description": "Include full output"}
            }
        }
    }
]

This enables the following:

• Custom tools that behave like typed function calls.

• Built-in Bash and text editor tools.

• Clear input/output schemas to ensure safe tool calling.

• Perfect for CI automation, local development workflows, or custom dev ops actions.

Tools transform Claude from a chatbot into a real-world operator.

A first-class agent citizen of MCP (Model Context Protocol)

Claude Code includes native MCP support, allowing it to integrate with structured external data sources (GitHub, Sentry, etc.). MCP servers act like adapters that connect Claude to APIs, databases, or internal systems.

response = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=4096,
    mcp_servers=[
        {"url": "mcp://github-server", "name": "github"},
        {"url": "mcp://sentry-server", "name": "sentry"}
    ],
    messages=[{
        "role": "user",
        "content": "Why did this deploy cause a latency spike?"
    }]
)

Using MCPs in this way enables:

• Claude can fetch GitHub PR diffs, CI logs, and deployment metadata.

• MCP server abstraction eliminates the need for API-key juggling and fragile HTTP calls.

• Your agent can correlate signals from multiple systems (GitHub + Sentry here).

MCP effectively gives Claude eyes and ears across your infrastructure.

Making agent memory native to Claude

Claude Code introduces a safe, structured memory system. When enabled, Claude can store working knowledge across multiple interactions, improving long-horizon tasks like debugging or development projects.

response = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=4096,
    tools=[{"type": "memory"}],
    messages=[{
        "role": "user",
        "content": "Debug why the payment service is timing out"
    }]
)

Using this Memory primitive, Claude can remember:

• The payment service uses a 30s timeout

• Retry logic lives in PaymentClient.java

• The team prefers a circuit breaker pattern

Memory allows for context accumulation — crucial for multi-day or multi-stage work.

Context editing in Claude Code

Large tool outputs (logs, diffs, JSON dumps) can quickly exhaust the context window. Claude Code introduces context editing to surgically remove or replace context segments.

response = client.messages.create(
    model="claude-sonnet-4-20250514",
    max_tokens=4096,
    messages=[...],
    context_management={
        "edits": [
            {"type": "clear_tool_uses_20250919"}
        ]
    }
)

Giving Claude Code the ability to control a fine-grained context enables better agentic workflows, such as:

• Remove old tool traces once processed.

• Keep only the meaningful summaries.

• Dramatically increase effective context length.

Your agent stays sharp, not bogged down by stale logs.

Code execution through a secure Ssandboxed runtime

Claude Code provides a remote, secure execution environment,  no need for local sandboxing, and no security risk to your machine.

response = client.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=8192,
    tools=[{"type": "code_execution_20250825"}],
    messages=[{
        "role": "user",
        "content": "Make the animation more sparkly"
    }]
)

Benefits of a sandbox environment:

• Run Python, JS, and other code safely.

• Generate and test animations, simulations, or data transforms.

• Offload compute-heavy tasks to secure Anthropic-managed VMs.

Claude becomes a true programmable agent, not just a generator.

Equipping the model with Agent Skills

Agent Skills are structured folders of scripts, markdown instructions, patterns, or domain knowledge that Claude can use autonomously. They pair perfectly with MCP:

• MCP gives Claude access to tools and context.

• Skills give Claude the expertise to use them effectively.

response = client.beta.messages.create(
    model="claude-sonnet-4-5-20250929",
    max_tokens=4096,
    container={
        "skills": [
            {
                "type": "user",
                "skill_id": "web-design-system",
                "version": "latest"
            }
        ]
    },
    messages=[{
        "role": "user",
        "content": "Build a landing page for our new product"
    }],
    tools=[{
        "type": "code_execution_20250825",
        "name": "code_execution"
    }]
)

Agent Skills natively allow for:

• Domain-specific skill packs (design systems, DevOps workflows, debugging expertise).

• Claude automatically decides when to use the skill.

• Combine skills, MCP, and tools for full agent autonomy.

Skills + MCP is where Claude transforms from a smart assistant into a full-stack autonomous agent.

Security guardrails for autonomous agents

Claude Code's new features, from Agent Skills and Tool Use to native MCP integration, are pushing the boundaries of autonomous development. But as your agents gain the power to write and execute code, they introduce new security risks at the speed of AI. Autonomous agents require security at inception.

The solution: Snyk Studio

Snyk Studio is the complete security solution for AI-driven development. It embeds Snyk's market-leading security intelligence directly into your AI-assisted workflows—like those powered by Claude Code—by leveraging the Snyk MCP Server.

Snyk Studio allows your agents to:

• Autonomously scan: Run Snyk security checks (Code, Open Source, IaC) before code is generated or committed.

• Enforce policy: Apply consistent security guardrails across all AI assistants.

• Fix at AI speed: Leverage intelligent, conversational remediation to clear security debt faster.

Dive deeper into AI agent security

For the deep technical dive, explore the pioneering work from Snyk Labs:

1. Secure the Agent's Context (MCP): See how the standard for secure AI communication is evolving. Read “Snyk Contributes New MCP Security Controls to the OWASP LLM Security Verification Standard.”

2. Test Your Agent’s Autonomy: Learn how to check your powerful LLM agents for vulnerabilities before they hit production. Explore the demo, “Red Team Your LLM Agents Before Attackers Do.”

Your agents are now connected. Are they secured?

Claude Code agents use the MCP Server to connect directly to your critical systems. This new point of integration is where modern security must be prioritized. Want to get the practical framework for protecting this vital new ecosystem? Download the guide, “Securing the MCP Server Ecosystem” today.