Agent Tools: Agent-Private Sandboxes with Embedded Tools may be the real future

Background

As AI agents become more capable, autonomous, and embedded in workflows, their ability to use tools effectively is becoming a core determinant of their utility.

Tools allow agents to go beyond generation and carry out real tasks by executing real them directly or performing the same via an API or functional interface. Since the time of elementary function calling, this interface has evolved rapidly over the past 12 months, with ideas, frameworks and protocols pitching to make interface standard, pluggable and reliable.

In this post, we examine why agent-private sandboxes represent the more scalable, secure, and may well be the answer to personalized, private and secure future for AI agent run time environment and tooling.

Agent Tools Interface Evolution

Agent tool interfaces are transforming to meet the growing needs of AI systems. Two dominant paradigms have emerged for tool interface:

• Tool Proxies via Model Context Protocol (MCP) or Custom Solutions (e.g., Zapier). Tools proxy, Tools framework or MCP servers have emerged as extensions to original function calling, to connect various actions directly to the agent orchestrator enabling agent to take real-world actions autonomously. Model Context Protocols and standardization around agent to tools interface needs a special mention on how it promises to be an ubiquitous open source standard around the same. For more on trade-offs and to MCP or not, you may wish to review the following blogs that Sanjeev Mohan and I published a few months ago.

1. To MCP or MCP not - Part 1 A Critical Analysis of Anthropic’s Model Context Protocol

2. To MCP or Not to MCP Part 2: Economic Impact of the MCP Standard

• Ephemeral Sandboxes as agent runtime environments: This represents an interesting evolution of tooling interface. For various diverse tasks such as code execution, web browsing, or computer interaction, you often need an ephemeral yet secure run time environment with relevant tools and libraries. These run time environments empower agents to interact with computers via native keyboard and mouse events, or often browser use for internet browsing, which may include filling forms, extracting web-pages, reasoning and further acquiring relevant information to satisfy user task. One can imagine almost all possible tasks that a human worker does on a console, including booking a ticket or a vacation plan or sending an email using email client or a combination of those. Some of the early examples of use of ephemeral sandboxes include the following:

Manus.im is a great example of a private sandbox used for performing general purpose long running tasks on behalf of the user. This may include writing code, browsing internet to research about a vacation or generation a good report

Anthropic's claude often uses similar environments for various web application rendering.

Perplexity.ai uses for data visualization related queries

Next Evolution: Agent-Private Sandboxes with Embedded Tools

However, the needs we know today continue to evolve and impact agent system design. They are likely to transform the current agent tooling interface in the next evolution.

Like the evolution that brought us until this point, we look at why Agent-Private Sandboxes with Embedded Tools likely represent the next evolution in the transformation of the tooling interface for most b2b or worker roles. These sandboxing environments are likely 1-1 mapped to the executing agent, likely permanent (and yet can be snapshotted, suspended when work is done to optimize costs, and resumed by agents as and when required.), may store states, may include tools pre-baked and dynamically added.

We examine some of the emerging requirements that will likey drive this evolution:

Long-running tasks require persistent environments. As agents take on more and more long-running tasks and potentially work throughout the length of the day like human workers, the agent environment must be available through the work needs of the agent. purpose built ephemeral sandboxes logically then loose value for such agentic needs. This is likely more closely resembling a human employee who is hired and often assigned a private laptop, desktop, or server vm environment with the necessary tools and access needed to carry out tasks effectively. Human agent can then make necessary changes, improve or enhance the work environment based within the larger governance needs of an enterprise.

Security and trust through environment isolation. The need for environments associated with unique AI agents carrying out long-running tasks also enable agents to run in a trusted and secure manner. Agent-private sandboxes address the need for security, privacy, and the ability to track individual agents.

Supporting agentic learning and improvement. Agentic architectures are evolving. We are likely moving from model learning into the era of agentic learning, self-reflection, and improvement. This will further require agents to continually acquire knowledge, train, test, and evaluate before putting newer models to work.

State persistence and knowledge management. Agents often need to store states and improve/extend their tooling, may need a scratchpad area while performing work and various other miscellenous requirements. A purpose built sandbox with tooling fit for the agent's role can help agents operate with more freedom.

Leveraging existing security infrastructure. Similarities with server/desktop environments used by humans enable these sandboxes to leverage the security and monitoring tools that are applicable today for our environments.

Comparision of popular agent tooling interface

External MCP Tool Proxies: Centralized Simplicity

In the current external proxy model, tools are hosted outside the agent runtime and exposed via an API through a proxy that speaks MCP. These tools can be centrally managed and updated independently of individual agents. This approach aligns with today's SaaS model, allowing tool reuse, governance, and control.

Pros:

• ✅ Centralized control: Tool updates, authentication, and monitoring happen in one place

• ✅ Resource efficiency: Tools are not duplicated across agents

• ✅ Rapid iteration: Easier to deploy new tools or upgrade existing ones

Cons:

• ❌ Latency: Every tool call introduces cross-system latency

• ❌ Limited personalization: Hard to customize tools for each agent's specific needs

• ❌ Privacy concerns: Tools operate on shared infrastructure, making data segregation harder

• ❌ Difficult offline usage: External tools assume constant connectivity

Agent-Private Sandboxes: Personalized, Secure, and Auditable

In this emerging model, each agent is provisioned with its own secure sandbox—think of it as a dedicated virtual environment, like an employee's laptop. Tools are packaged with the agent, possibly during deployment or dynamically loaded. The agent has direct access to its tools within this private context.

Pros:

• ✅ Security and data isolation: Each sandbox can have access control, audit logging, and strict boundaries—ideal for sensitive environments like healthcare, finance, or government

• ✅ Customizability: Tools can be tailored to an agent's personality, role, and task environment

• ✅ Better observability: Full visibility into the agent's code execution and side effects

• ✅ Supports continuous learning: The sandbox can persist state and history to evolve with the agent

• ✅ Offline-first: Agents can run even in low or no connectivity scenarios

Cons:

• ❌ Deployment overhead: Requires sandbox infrastructure per agent or per user

• ❌ Resource duplication: May need to replicate tools across many agents

• ❌ Update complexity: Tools in private sandboxes may lag behind centrally managed versions unless syncing is automated

Agent-Private Sandboxes: Will it be the next dominant tooling interface?

The trajectory of AI agent development mirrors that of human employees: as they become more capable and embedded in organizations, they will need individualized environments that reflect their roles, permissions, and history.

A one-size-fits-all tool proxy won't suffice. Additionally, Seperation of MCP Proxies and Ephemeral Sandboxes for different needs seems like an artificial line.

Agent-private sandboxes with embedded tools represent an interesting fusion of both of the above options. Beyond allowing agents with a personalized execution with general purpose tools, it may specifically be baked with tools specific for the agent roles via MCP extension. Benefits of such approach also include full privacy, security, reproducibility, and fine-grained control—all essential for trustworthy autonomy.

Agent private sandboxes are easy to understand since this is just replicating the current IT process. Just as companies provision laptops with specific software stacks, security profiles, and access controls for employees, they'll do the same for agents. These sandboxes will:

• Log every command and side effect

• Allow sandbox-specific tuning and toolchains

• Enable safe experimentation and rollback

• Provide local reasoning context unavailable to central services

Conclusion

External tool proxies solved the initial tool integration problem for LLMs. Ephemeral Sandboxes solved the execution and run-time environments for special task needs. But as agentic automation mature from copilots to task based coworkers to full-time workers, the next evolution of the agent toolbox lies in agent-private sandboxes with embedded tools—secure, personalized, persistent environments that reflect the agent's identity, context, and role.