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Agent Herbie

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About

Agent Herbie is an AI agent platform designed for real-time, mission-critical operations within private and highly regulated environments. Developed by Broadn, the system focuses on an "offline-by-default" architecture, allowing organizations in sectors like finance, manufacturing, and defense to automate complex processes without the security risks associated with public cloud data egress. The platform functions as a distributed agent that can be deployed via Docker on local hardware, from edge devices to centralized server swarms, utilizing both CPU and GPU resources to process data streams, detect patterns, and trigger automated actions with zero external connectivity. The tool works by establishing a hierarchical "monitor-assess-react" loop. It integrates with various data sources, including inbound HTTP endpoints, raw TCP/UDP streams, database tables, and IoT sensors. For analysis, it employs a hybrid approach: it uses small, portable on-device LLMs—such as Llama, Qwen, or Phi-4 variants—for entity extraction and classification, alongside classical machine learning and statistical methods like ARIMA, PCA, and drift detection for deterministic processing. Once a decision is reached based on user-defined thresholds or multi-signal logic, the agent acts by updating systems of record, opening support tickets, or directly commanding hardware and robotics. This platform is specifically built for industries where data sovereignty and uptime are non-negotiable. In financial services, it handles document-to-case automation and ledger reconciliation while maintaining compliance with SEC and FINRA standards. Manufacturing teams use it for predictive maintenance and real-time statistical process control (SPC) that remains functional even during wide area network (WAN) outages. Its ability to operate in air-gapped environments makes it suitable for public sector and defense operations requiring CJIS-aligned controls and immutable audit trails. What differentiates Agent Herbie from typical AI assistants is its commitment to zero data egress and its focus on local model execution. While it can connect to cloud providers like Anthropic or OpenAI via private gateways if requested, its core strength lies in running 500M to 8B parameter models locally. This ensures that intellectual property and sensitive data, such as PHI in healthcare or proprietary trade secrets in finance, never leave the perimeter. Furthermore, the platform includes advanced security features like post-quantum cryptography modules and mutual TLS, providing a level of protection required for the most sensitive enterprise environments.

Pros & Cons

Operates entirely offline to prevent data leaks and intellectual property theft.

Reduces decision latency by 90% by eliminating cloud round-trips for real-time tasks.

Combines LLMs with classical statistical methods for highly deterministic and precise results.

Supports deployment on diverse hardware from edge CPU devices to high-end GPU swarms.

Includes built-in modules for post-quantum security and mutual TLS encryption.

Requires a technical team to manage Docker-based deployments and on-prem infrastructure.

Local model performance (8B parameters) may be less capable than massive 1T+ parameter cloud models.

Workflow setup involves complex configuration of thresholds, counters, and rolling windows.

Pricing is not transparently listed and requires a direct sales contact and demo.

Use Cases

Financial compliance officers can automate ledger reconciliation and trade confirmations without sensitive records leaving the corporate network.

Manufacturing engineers can deploy edge agents for predictive maintenance and line controls that remain operational during total WAN failures.

Defense and public sector teams can process classified document routing and retention schedules within fully air-gapped secure facilities.

Healthcare administrators can extract structured data from medical records locally to automate prior authorizations while ensuring PHI remains within the perimeter.

Critical infrastructure operators can correlate SCADA feeds and trigger emergency isolation runbooks aligned with NERC CIP requirements.

Platform
Web
Task
operations automation

Features

docker-based deployment

multi-signal logic gates

immutable audit trails

post-quantum cryptography

statistical drift detection

air-gapped deployment

offline llm inference

zero data egress

FAQs

Does Agent Herbie require an internet connection to function?

No, Agent Herbie is designed for fully offline operation. It can be deployed in air-gapped environments where it processes data, runs models, and triggers actions without any external data egress.

Which AI models can be run locally on the platform?

The platform supports small, portable models including the Llama family, Qwen, Phi-4-Reasoning, and Mamba-based models. These typically range from 500M to 8Bn parameters and are trained on proprietary data.

What kind of hardware is required for deployment?

Agent Herbie is highly flexible and can be deployed via Docker on hardware ranging from CPU-only edge devices to GPU-accelerated server swarms, depending on the complexity of the workload.

How does the tool ensure regulatory compliance?

It provides immutable audit trails, encryption at rest with customer-managed keys, and mutual TLS. The architecture is designed to meet standards such as SEC 17a-4, CJIS, HIPAA, and NERC CIP.

Can I still use cloud-based models like GPT-4 or Claude?

Yes, the platform offers a 'Cloud-Connected' mode. You can route to approved cloud providers via private gateways with strict routing and your own API keys while maintaining selective data access.

Pricing Plans

Enterprise
Unknown Price

Zero data egress by default

Air-gapped operation support

On-device LLM deployment

Deterministic statistical processing

Command Center observability

Immutable audit trails

Post-quantum security module

Custom workflow automation

Role-based access control (RBAC)

Fleet topology monitoring

Job Opportunities

There are currently no job postings for this AI tool.

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