Run real code in fully isolated containers — Python, R, shell, SQL — with enterprise-grade security, audit trails, and zero risk to your host infrastructure.
AI agents that can only generate text are half an agent. ibl.ai enables agents to execute real code — Python scripts, R models, shell commands, SQL queries — inside fully isolated containers that are completely separated from your host systems.
Built on the enterprise-hardened OpenClaw framework, ibl.ai's sandboxed execution environment gives agents the computational power to solve real problems: running data pipelines, installing packages, querying databases, and automating workflows — all within strict security boundaries.
With three layered security models — NanoClaw, IronClaw, and OpenClaw application-level controls — organizations get defense-in-depth isolation that satisfies compliance requirements without sacrificing agent capability. Trusted by 400+ organizations and 1.6M+ users in production.
Most enterprise AI deployments hit a hard ceiling: the agent can reason about a problem but cannot act on it. Generating a Python script is not the same as running one. Without sandboxed execution, agents are advisory tools — they describe what should be done but cannot do it, forcing humans back into the loop for every computational task.
Worse, organizations that attempt to give agents direct system access face catastrophic risk. An agent with unrestricted access to a host environment can overwrite files, exfiltrate data, exhaust resources, or trigger cascading failures. Without container isolation, network restrictions, resource limits, and audit trails, real code execution is an unacceptable security liability — leaving enterprises stuck between capability and safety.
LLM-only agents can describe code but cannot execute it. Tasks requiring data transformation, statistical modeling, or system automation remain incomplete without a human executing the generated output.
Agents become expensive suggestion engines rather than autonomous workers, defeating the ROI case for agentic AI deployment.Granting agents direct access to host systems — file systems, network interfaces, credentials — creates unacceptable attack surfaces. A single misconfigured agent or prompt injection can compromise the entire environment.
Security and compliance teams block production deployment, stalling AI initiatives indefinitely.Without structured logging of every code execution, file access, and network call, organizations cannot demonstrate compliance, investigate incidents, or hold agent behavior accountable to policy.
Regulated industries — finance, healthcare, government — cannot deploy agents that lack verifiable audit trails, creating a hard blocker for enterprise adoption.Agents executing code without resource limits can consume unbounded CPU, memory, or disk — crashing shared infrastructure, starving other workloads, or generating runaway costs in cloud environments.
A single poorly scoped agent task can destabilize production infrastructure, eroding trust in the entire agentic platform.Agents requiring specific package versions, runtime environments, or system libraries create dependency conflicts when sharing a host. Managing per-agent environments manually is operationally unsustainable at scale.
Engineering teams spend more time managing agent environments than building agent capabilities, slowing deployment velocity.The OpenClaw Brain receives a task via any of 12+ supported channels — Slack, Teams, API, or scheduled Heartbeat trigger. The ReAct reasoning loop determines that code execution is required and selects the appropriate skill from 5,700+ available plugins.
A fresh, ephemeral Linux container is spun up for the agent session. Under NanoClaw, OS-level isolation is enforced in ~500 lines of auditable code. Under IronClaw, five independent security layers activate: network restrictions, request filtering, credential isolation, WASM sandboxing, and Docker containment.
The agent executes Python, R, shell scripts, or SQL queries inside the container. It can install packages, read and write files within its scoped file system, query databases, and browse the web — all within defined permission boundaries and resource limits.
Execution outputs — stdout, stderr, generated files, query results — are captured and written to the agent's persistent memory layer (Markdown files + SQLite vector/keyword search). Results are available for subsequent reasoning steps or downstream agent tasks.
Every code execution event, file access, network call, and permission check is logged to an immutable audit trail. Logs include timestamps, agent identity, skill invoked, resource consumption, and execution outcome — satisfying compliance and forensic requirements.
On task completion or timeout, the container is torn down and all ephemeral state is purged. Only explicitly persisted outputs survive. Host systems remain completely unaffected, and resource limits are enforced throughout the lifecycle.
Agents execute Python, R, shell scripts, SQL, and more inside sandboxed containers. Custom packages can be installed per-session without affecting host environments or other agent workloads.
NanoClaw provides OS-level container isolation in ~500 lines of auditable code. IronClaw adds five independent security layers: network restrictions, request filtering, credential isolation, WASM sandboxing, and Docker containment. OpenClaw enforces application-level per-user and per-skill permission checks.
Every agent action inside the sandbox — code executed, files accessed, network calls made, packages installed — is logged with full context. Audit logs support compliance reporting, incident investigation, and policy enforcement.
CPU, memory, disk I/O, and network bandwidth limits are enforced at the container level. Runaway processes are automatically terminated. Resource quotas can be configured per agent, per skill, or per organizational unit.
Unlike stateless sandboxes, ibl.ai agents retain execution outputs, generated files, and learned context across sessions via Markdown-based persistent memory and SQLite vector search — enabling multi-step, long-horizon workflows.
Sandboxed execution runs on your infrastructure — on-premises, private cloud, AWS, GCP, or Azure. No data leaves your environment. Fully compatible with air-gapped deployments for defense and government use cases.
The sandbox layer is decoupled from the LLM. Organizations can run sandboxed agents on GPT-4, Claude, Gemini, Llama, or any custom model — switching or mixing models without changing execution infrastructure.
| Aspect | Without | With ibl.ai |
|---|---|---|
| Code Execution Capability | Agents generate code as text output; a human must copy, paste, and run it manually | Agents execute Python, R, shell, and SQL directly inside sandboxed containers with results returned to the agent in real time |
| Host System Security | Granting agents system access exposes host file systems, credentials, and network interfaces to agent errors or prompt injection attacks | Container isolation, network restrictions, and credential separation ensure zero host exposure regardless of agent behavior inside the sandbox |
| Compliance and Audit | No structured record of what code ran, what data was accessed, or what outputs were produced — failing audit requirements in regulated industries | Immutable, structured audit trails capture every execution event with full context, satisfying SOC 2, HIPAA, FedRAMP, and internal governance requirements |
| Package and Dependency Management | Agents cannot install packages; they are limited to pre-installed libraries, blocking specialized analytical or scientific workflows | Agents install any required packages (pip, conda, apt) inside ephemeral containers without affecting host environments or other agent sessions |
| Resource Safety | Runaway agent processes can exhaust host CPU, memory, or disk — destabilizing shared infrastructure and generating unbounded cloud costs | Hard resource limits (CPU, RAM, disk, timeout) are enforced at the container level; runaway processes are automatically terminated |
| Memory and State Persistence | Each execution is stateless; agents cannot build on prior results, forcing repetitive re-computation and losing analytical context between sessions | Execution outputs are persisted to the agent's memory layer (Markdown + SQLite) and available for subsequent reasoning steps and future sessions |
| Infrastructure Flexibility | Vendor-hosted sandboxes (GPTs, Gems) lock execution to the vendor's cloud, blocking on-premises, air-gapped, or sovereign cloud deployments | Sandboxed execution runs on any infrastructure — on-premises, private cloud, AWS, GCP, Azure, or air-gapped environments — with full data sovereignty |
Agents generate code as text output; a human must copy, paste, and run it manually
Agents execute Python, R, shell, and SQL directly inside sandboxed containers with results returned to the agent in real time
Granting agents system access exposes host file systems, credentials, and network interfaces to agent errors or prompt injection attacks
Container isolation, network restrictions, and credential separation ensure zero host exposure regardless of agent behavior inside the sandbox
No structured record of what code ran, what data was accessed, or what outputs were produced — failing audit requirements in regulated industries
Immutable, structured audit trails capture every execution event with full context, satisfying SOC 2, HIPAA, FedRAMP, and internal governance requirements
Agents cannot install packages; they are limited to pre-installed libraries, blocking specialized analytical or scientific workflows
Agents install any required packages (pip, conda, apt) inside ephemeral containers without affecting host environments or other agent sessions
Runaway agent processes can exhaust host CPU, memory, or disk — destabilizing shared infrastructure and generating unbounded cloud costs
Hard resource limits (CPU, RAM, disk, timeout) are enforced at the container level; runaway processes are automatically terminated
Each execution is stateless; agents cannot build on prior results, forcing repetitive re-computation and losing analytical context between sessions
Execution outputs are persisted to the agent's memory layer (Markdown + SQLite) and available for subsequent reasoning steps and future sessions
Vendor-hosted sandboxes (GPTs, Gems) lock execution to the vendor's cloud, blocking on-premises, air-gapped, or sovereign cloud deployments
Sandboxed execution runs on any infrastructure — on-premises, private cloud, AWS, GCP, Azure, or air-gapped environments — with full data sovereignty
Enables autonomous computational workflows on sensitive data without any risk of host system exposure or data exfiltration.
Satisfies SOX, MiFID II, and internal audit requirements while enabling real-time autonomous financial analysis.
Accelerates research workflows while maintaining strict PHI isolation and generating the audit evidence required for regulatory submissions.
Reduces manual review hours while producing defensible audit trails that satisfy legal hold and e-discovery requirements.
Enables autonomous IT operations with zero risk of agents inadvertently modifying production infrastructure.
Researchers gain autonomous computational assistants that execute experiments overnight without human supervision, accelerating research velocity.
Reduces actuarial cycle times from days to hours while maintaining the audit evidence required for regulatory and internal risk governance.
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.