The Problem: AI Agents Running Amok on Developer Laptops
The rise of AI coding agents has brought unprecedented productivity to software development, but it has also exposed a fundamental mismatch: these agents are designed to run on individual developer machines, a model that no longer works. According to Incredibuild, a company recognized for its build acceleration platform used by major firms like Microsoft, Take-Two, and Nintendo, the current approach leads to inefficiencies, security risks, and unsustainable workflows. As Adam Gold, Director of Product Engineering at Incredibuild, explains, "Coding agents are capable of doing real work now, but they all run on the developer's laptop. That means they die when the lid closes, and they have access to everything on the machine." This one-developer, one-machine model fails to accommodate agents in three distinct ways.
Lifecycles Out of Sync
Human developers have predictable schedules: they work, close the lid, and resume the next day. Agents, however, have continuous tasks that don't align with human sleep or breaks. The result? Developers have been observed walking around with laptops half-open just to keep agents running. Incredibuild flatly describes this as "not a workflow." Agents need a persistent lifecycle that doesn't depend on human supervision.
Security Blast Radius
When an agent runs on a developer's machine, it inherits every credential the developer has accumulated—SSH keys, AWS profiles, browser cookies, and more. Unlike a human, an agent lacks judgment about when to use these credentials. This creates a large blast radius: if an agent is compromised or makes a mistake, it can access sensitive systems without oversight.
Need for Persistent Environments
Agents require warm, persistent environments with running services, databases, and build caches. Ephemeral containers, which are torn down after each run, discard this state, forcing agents to start from scratch every time. This is inefficient and incompatible with the complex workflows agents are designed to handle.
The Solution: Islo – A Dedicated Sandbox for Every Agent
To address these issues, Incredibuild has introduced Islo, a sandbox purpose-built for AI coding agents. The core idea is simple: every agent gets its own persistent, isolated cloud environment with explicit governance policies. As Gold puts it, "We built Islo because we believe that every AI agent needs its own computer—not an ephemeral container, but a long-running dev environment with its own running services, scoped credentials, and a lifecycle that doesn't depend on human supervision." This departure from the one-developer, one-machine model ensures agents can operate continuously, securely, and efficiently.
Persistent, Isolated Cloud Environments
Islo provides each agent with a dedicated virtual machine that remains active regardless of the developer's actions. The agent can run services, maintain databases, and keep build caches across sessions. This persistence eliminates the need to keep laptops open or restart tasks. The isolation also prevents agents from interfering with each other or with the developer's primary environment.
Scoped Credentials and Governance Policies
Security is a primary concern. Instead of inheriting the developer's full set of credentials, each agent in Islo receives scoped permissions that are explicitly defined by policies. This minimizes the blast radius: agents can only access what they need, and any misuse is contained. Teams can also set governance rules for agent behavior, ensuring compliance with corporate security standards.
Why Existing Solutions Fall Short
Incredibuild distinguishes Islo from current alternatives like cloud development environments (e.g., GitHub Codespaces, Daytona, Coder). These tools were built for humans, not agents. They assume an IDE is attached, they idle out after inactivity, and their security model trusts the human developer. As noted earlier, agents outlive human sessions and don't have the same judgment, making such environments unsuitable.
Not for Human Developers
Cloud dev environments are optimized for interactive use—they spin up when a developer connects and idle when not in use. Agents, however, need constant uptime and don't require a graphical interface. Using these environments for agents leads to premature termination or wasted resources.
Ephemeral vs Persistent
Many container-based solutions offer ephemeral environments, but as argued in the persistent needs section, agents benefit from a warm state. Islo's persistent approach avoids the overhead of rebuilding caches and restarting services on every run, which is a critical advantage for long-running or iterative tasks.
Conclusion: A New Model for Agent Development
The era of AI coding agents demands a shift from the one-developer, one-machine paradigm. Incredibuild's Islo offers a dedicated, secure, and persistent environment for each agent, solving the lifecycle, security, and resource challenges that plague current setups. By giving every agent its own computer—not just a container—Incredibuild is paving the way for agents to work autonomously and safely, without relying on human supervision or straining developer workflows. This innovation could redefine how enterprise teams integrate AI into their development processes, making agents a reliable and scalable addition rather than a source of chaos.