Reduce MTTR by up to 90%
Boost NOC productivity by 75%
Fast, scalable & secure
Lightning Fast AI Agents
Nanites is a system of specialized AI agents that reason and safely take action across your entire network, just like a human network engineer, but up to 100x faster. They work in parallel, making the system incredibly fast. Tasks that used to take hours now take minutes or even seconds. The system uses existing tools and safely accesses network devices to correlate telemetry, troubleshoot issues, and prepare fixes for approval, always with a human in the loop.
Founded by domain experts
Nanites was founded by experts from leading tech companies and universities. Our founding team includes CCIE-certified network engineers and cybersecurity specialists, bringing 34 years of combined domain experience across enterprise, service provider, and data center networks. We have deployed networks for startups to Fortune 500 companies and experienced the challenges firsthand.
The Problem
Networks are hard to troubleshoot because the evidence is fragmented across disparate vendor devices, tools, OSes, telemetry, logs, and configs.
Nanites safely investigates incidents like a NOC engineer: analyzes alerts, runs checks in parallel, correlates the story, opens a ticket, notifies the engineer, and proposes a safe, scoped fix.
How Nanites works
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Trigger & Goal
Triggered by an alert or user input, then establishes the goal and pulls the relevant context.
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Investigate
Queries devices and data sources to gather live or historical signals from the network.
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Diagnose & Next Steps
Correlates findings to identify root cause, then recommends next steps.
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Remediate & Verify
A human makes the change, then Nanites verifies the issue is resolved.
The system plugs into your existing telemetry, monitoring, ticketing tools, and data sources, then correlates metrics, logs, and alerts end-to-end. No rip-and-replace. Nanites ties everything together and makes your current stack smarter.
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Uses your existing tools
Built for Network Engineers
Nanites logs into devices just like you do and runs real read-only commands across disparate vendors. It reasons over raw outputs instead of abstract dashboards or wrappers, so it sees the same truth you see.
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Speaks your device’s CLI
Every agent workflow is guardrailed and explainable. Nanites documents each step and proposes a safe, scoped fix.
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Safe, deterministic workflows
From data centers to service provider networks, Nanites is built to handle mixed vendors, OSs, and topologies. It treats your heterogeneous tech stack as a single system.
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Multi-vendor by design
Nanites is always on call for noisy, late-night incidents: it triages alerts, homes in on probable root causes, and assembles ready-to-apply fixes up to 100x faster than traditional workflows, so you spend less time firefighting and more time engineering.
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On-call 24/7
FAQ
In networking, the cost of a wrong action isn’t “a bug,” it’s an outage.
We engineered Nanites so you don’t have to “trust the LLM” to behave. In production, Nanites runs in read-only mode: it can observe, collect signals, and run diagnostic commands, but it does not have write access to your devices.
Guardrails are enforced outside the agent at both the application layer (Nanites’ tools) and the device layer (read-only access), so even if an LLM were to generate something unsafe, the underlying execution path won’t allow destructive or unauthorized operations.
When remediation is needed, Nanites can recommend the exact change and verification steps, but applying changes is kept under explicit operator control.Nanites thinks and acts like a network engineer. It is not running predefined scripts or playbooks.
It understands context, reasons about what is happening, logs into devices, uses your existing tools, accesses live data sources, correlates telemetry, troubleshoots, and proposes fixes across networks of any size and vendor mix. It does all of this deterministically and safely inside your environment, using the same workflows your engineers use, up to 100x faster, and always with a human in the loop.
Although the system can run predefined scripts or playbooks, the core system is fully agentic and determines its own guardrailed workflows.
From what we have seen, most agentic systems in networking today sit on top of a few tools, analyze alerts, or suggest commands that an engineer still has to run. Nanites is an agentic, AI-native system that behaves like a real network engineer end-to-end. We have been building it as a fully agentic system since early 2024, not as a bolt-on or pivot.
This is one of the hardest problems in AI today, because it requires deep domain expertise and thousands of evaluations to tune the system across heterogeneous devices and complex use cases.
To learn more and watch a demo, see our blog "The Role of AI Agents in Network Automation".Our system operates semi-autonomously when triggered or given a goal.
Nanites is in early access with select design partners. We’re validating reliability, guardrails, and workflows in controlled lab and pilot environments before broader production availability.
Nanites is currently in read-only mode. Write permissions are disabled.
We can enable write permissions in a testing environment only. Nanites will only make changes with explicit human approval. It shows the exact commands/diff first, explains why, logs what happened for auditability, and supports rollback.
Some capabilities are still under active development and testing.Nanites is engineered with security as the first priority. It uses least-privilege access to your tools and devices and never uses your data to train external models. All communication is encrypted, every action is logged, and the system cannot make configuration changes without explicit human approval. Role-based controls and strict policy boundaries prevent unauthorized access or data exfiltration, both internally and externally. We follow best practices for securing agentic AI systems, including mitigations for prompt injection and agent poisoning. Because this is a novel and rapidly evolving field, some advanced protections are still under active development.
Nanites connects to your systems the same way your engineers do today. It collects telemetry from devices using protocols like gNMI, SNMP, syslog, and streaming telemetry, and logs into routers, switches, and firewalls over SSH and APIs such as NETCONF and RESTCONF. It integrates with your existing tools and data stores through their APIs, for example time-series databases and monitoring platforms. You control which accounts and permissions it uses.
Our system supports networks of any size or type; however, it delivers the greatest value in large-scale, complex environments like service provider, data center, and enterprise networks.
Our system can be deployed on-premises or in the cloud.
No, our system integrates seamlessly with your existing tools, systems, and workflows. It is an AI network engineer that interacts with your existing tech stack just like a human, making it easy to onboard.
Nanites are microscopic machines or nanobots, often conceived in science fiction, that are capable of autonomously performing a wide array of tasks at a nanoscale. In our context, they are fleets of AI agents, much like swarms of bees, working together in parallel to perform time-consuming networking tasks.
FAQ
Nanites are AI network engineers that troubleshoot production networks at machine speed.
The system responds to human questions and alerts, determines what information is needed, collects relevant data, reasons over that data, and explains what can and cannot be concluded.
Nanites is engineered for production networks where safety, accuracy, latency, cost, access control, and operational trust matter.
Like a human network engineer, Nanites interprets the user request or alert, reasons about the issue, determines what information is needed, and safely collects relevant data from authorized infrastructure and operational systems, including live network devices, telemetry, flow data, logs, alerts, sources of truth, ticketing systems, and documentation.
It synthesizes information, evaluates what it finds, and develops and tests hypotheses.
Nanites does not rely on a fixed library of scripts, runbooks, or predefined workflows. Instead, it dynamically generates workflows based on the available context, the systems and data sources it is authorized to access, and what it discovers along the way.
The system adapts its reasoning and next steps as new information becomes available.
Nanites supports a broad range of network troubleshooting, incident investigation, and operational analysis use cases for data center operators, AI factories, neoclouds, service providers, and enterprise network teams.
Representative use cases include:
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Device reachability and availability issues
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Interface errors, packet loss, congestion, and intermittent links
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Routing and control-plane problems
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Layer 2, VLAN, spanning-tree, loop, and forwarding issues
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Device reboots and unexpected state changes
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MAC address, route, and network path tracing
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Topology and asset discovery
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Alert-driven incident investigation
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Multi-device and cross-system root-cause analysis
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Performance and infrastructure health analysis
These are examples, not a complete list. Nanites dynamically generates the troubleshooting process based on the available infrastructure data, the systems it is authorized to access, and what it discovers along the way.
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General-purpose AI assistants and coding agents such as Claude Code and Codex can generate network configurations and automation, execute CLI commands, access systems through terminals, APIs, MCP, or other tools, and assist with troubleshooting when appropriately configured and supervised.
They are designed for broad use, however. Applying them consistently and safely across production networks requires the operator to build and maintain the network-specific tooling, access controls, command boundaries, context, validation, and operational workflows around them.
Nanites is a purpose-built operational system for network operations. It can determine what information is needed, identify the appropriate devices and data sources, safely collect that information, evaluate findings across the environment, and adapt its reasoning and next steps as new information becomes available.
The system incorporates network-specific knowledge developed through thousands of evaluations across devices, network operating systems, CLI commands, model paths, and troubleshooting scenarios. This includes handling platform-specific behavior, selecting the information most likely to matter, interpreting large and inconsistent outputs, and locating relevant signals across multiple systems.
Our engineers evaluate different AI models across the specific roles they perform within the system, including planning, routing, data collection, interpretation, summarization, critical reasoning, and final response generation. This allows Nanites to understand where each model performs reliably, where it does not, and which model is appropriate for each part of the operational workflow.
The system is engineered to support validated commercial and open-weight models, reducing dependence on any single model provider while allowing deployment requirements, performance, cost, and data-handling policies to be considered.
Nanites is also designed to operate across network vendors through their native operational interfaces, including the CLI, rather than depending on a proprietary control plane from a single equipment vendor.
Because these collection and reasoning paths are already engineered and evaluated, Nanites can investigate network issues substantially faster and more accurately than a general-purpose agent being adapted to the environment during each interaction.
Nanites also includes production controls for target selection, command execution, access, timeouts, concurrency, output limits, and auditability. It distinguishes between what was verified, what was inferred, what remains uncertain, and what additional information may be required.
Nanites is read-only by default. Changes, when enabled, are handled through explicit permissions, approvals, and controlled execution rather than unrestricted model access.
Organizations can build useful internal network AI tools using general-purpose models, coding agents, MCP, APIs, and existing automation. For a narrow environment or well-defined use case, a capable engineering team may be able to build a useful prototype relatively quickly.
The harder part is the harness around the model. A production system must determine what data is relevant, retrieve it safely from the correct devices and systems, reduce large and inconsistent outputs, maintain context across an investigation, distinguish verified findings from inference, and prevent the model from exceeding its authorized scope.
It also requires extensive evaluation. Models must be tested across the specific roles they perform, including planning, command selection, routing, interpretation, summarization, critical reasoning, and final response generation. Those behaviors can vary across models, providers, network operating systems, software versions, commands, and troubleshooting scenarios.
Safety requires more than instructions in a prompt. Teams must implement deterministic command validation, target controls, least-privilege access, timeouts, concurrency limits, auditability, approval paths, and fail-closed behavior around unsupported or ambiguous actions.
Building internally may make sense for organizations with a narrow use case, specialized requirements, and the engineering capacity to develop and continuously maintain the system. Nanites provides a purpose-built, multi-vendor operational platform for organizations that do not want to assemble and validate the complete network-specific AI harness themselves.
Nanites is currently working with a limited number of early customers and partners.
The system has been validated across multiple network environments and network operating systems, but it is not yet generally available as a fully standardized, self-service product for every production environment.
Deployments are currently evaluated individually based on the customer’s use cases, network architecture, security requirements, integrations, and operating systems.
Nanites is designed around bounded access, least privilege, defense in depth, and human oversight.
By default, Nanites operates in read-only mode. Only approved tools are exposed to the system, and access can be restricted by network operating system, device type, user role, inventory, and deployment policy.
Safety-critical controls are enforced deterministically at the tool and infrastructure layers rather than relying on the AI model to follow instructions. Unknown or unsupported commands fail closed, device targets are resolved against operator-controlled inventory, and the model cannot register new tools, specify arbitrary hosts, or disable its own controls.
The system also blocks shell chaining, redirection, command injection, unsupported commands, unrestricted targeting, and unauthorized access to sensitive files or configuration data. Output limits, concurrency controls, timeouts, rate limits, and device scoping help prevent excessive or unbounded execution.
Where supported, device-level AAA provides an additional independent backstop, so write operations are rejected even if an application-layer control were to fail. Command attempts and execution decisions are logged for auditability.
No AI system should be trusted solely because it produces a confident answer. Nanites is engineered to show the basis for its conclusions, distinguish verified findings from inference, and clearly identify information that could not be confirmed.
Changes, when enabled, are handled through explicit permissions, approvals, policy controls, and auditable execution rather than unrestricted model autonomy.
No.
Nanites can autonomously perform bounded troubleshooting tasks within the permissions and policies defined for a deployment. It is not intended to operate as an unrestricted autonomous administrator.
The system may decide which authorized information to collect, which devices to examine, and which troubleshooting steps to perform. Actions remain constrained by access policies, command controls, target boundaries, and workflow limits.
Nanites is read-only by default.
Configuration changes may be supported through controlled workflows that require explicit authorization, policy checks, human approval, scoped credentials, and an auditable record of the requested action.
Nanites is not designed to make unrestricted changes directly to production networks.
The availability of change workflows depends on the customer environment, use case, approval model, and deployment policy.
Nanites is engineered with infrastructure security, deployment flexibility, and model governance in mind.
Nanites is LLM-agnostic and supports customer-approved commercial and open-weight model deployments. The models available within a deployment are determined by the customer’s security, compliance, procurement, and data sovereignty requirements.
Because Nanites works with live network infrastructure and safety-sensitive operational data, models are not treated as interchangeable. Every model, deployment path, and intended use case must be validated by our team through evals for accuracy, reliability, latency, safety, and operational suitability before it is enabled for production.
Customer production data, including network topology, device output, logs, configurations, telemetry, and other operational context, is processed only through customer-approved model endpoints and deployment paths under documented privacy, retention, access-control, and security policies.
For U.S. government, critical infrastructure, telecommunications, and other security-sensitive environments, Nanites supports strict model governance. Deployments can be configured to allow only approved model families and endpoints that satisfy the customer’s security, compliance, procurement, and data-handling requirements. Restricted model families can be explicitly disabled, and all model selection and execution remains under customer control.
Nanites connects through customer-approved access paths.
Depending on the environment, this may include SSH, APIs, MCP, telemetry systems, network management platforms, alerting tools, log systems, topology sources, or customer-controlled jump hosts.
Credentials and permissions can be scoped to specific devices, commands, environments, and use cases.
Nanites does not require unrestricted administrative access. The preferred model is least-privilege access limited to the information needed for approved workflows.
Nanites supports flexible deployment models based on customer security, infrastructure, and data-handling requirements.
Depending on the environment, components may be deployed:
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In a customer-controlled environment
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On premises
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In a private cloud or virtual private cloud
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In a restricted network environment
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Through a hybrid architecture
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With approved cloud-based model endpoints
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With approved private or customer-controlled model endpoints
Device access and sensitive integrations can remain inside the customer environment while other system components are deployed according to customer policy.
The final architecture is determined through a deployment and security review.
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No.
Nanites is designed to work with the systems customers already use.
It can integrate with network devices, telemetry platforms, log systems, alerting tools, monitoring systems, ticketing workflows, topology sources, and other operational platforms.
Nanites is intended to help connect information across these systems and reduce the manual effort required to investigate network issues.
Available integrations depend on the customer environment and deployment scope.
Nanites has been validated against:
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Cisco IOS XE
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Cisco IOS XR
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OcNOS
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MikroTik RouterOS
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SONiC
Support is based on specific commands, outputs, workflows, and software versions rather than vendor name alone.
Additional network operating systems can be evaluated and onboarded based on customer requirements. Each new platform is tested before being approved for production workflows.
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Deployment time depends on the environment.
The main factors include:
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Network size and complexity
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Supported network operating systems
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Access and security requirements
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Available topology information
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Monitoring and alert integrations
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Required use cases
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Deployment model
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Customer change-control processes
A limited proof of value can usually be scoped more narrowly than a production deployment. Production use requires additional validation, security review, integration work, and testing against the customer’s real workflows.
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Nanites is engineered to communicate uncertainty rather than manufacture an answer.
When the available information is insufficient, the system should identify:-
What was verified
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What was not verified
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Which hypotheses remain possible
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What information is missing
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Which additional checks may help
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Whether human investigation is required
A useful operational system must know the limits of the information available to it. Nanites is engineered to support that standard rather than treating every request as if it has a definitive answer.-