Governed AI Agent Orchestration for Tactical Edge and Hybrid Cloud
Guidepad helps public sector organizations put AI agents to work across heterogeneous, distributed systems with control over the data they access, the actions they take, and the people responsible for approving consequential decisions. The platform brings AI agent orchestration, workflow automation, data integration, and DevSecOps automation together across existing government systems. Agencies can connect information, coordinate operations, and introduce AI into real workflows while retaining oversight of mission activities.
For defense, intelligence, and civilian agencies, deploying AI involves more than choosing a model. Teams must connect legacy applications, protect sensitive information, manage approvals, and operate across environments with different security and connectivity requirements. Guidepad addresses these needs through a governed runtime that applies policy checks, access controls, human approval where required, and audit trails to operational actions.
AI agents run as governed services within this environment. Agents propose actions; policy checks and designated operators authorize them as required; Guidepad executes and records. Each agent runs inside an explicit state machine that bounds what it can do, credentials are scoped per step and kept outside the agent’s context, and every run leaves a replayable record. Because every Operation emits events and listeners trigger the next action when those events arrive—across a federated network of instances—agents and automations can respond as events occur. Agents can participate in workflows that retrieve information, validate submissions, analyze records, and prepare outputs, with controls governing when and how changes reach underlying systems.
Guidepad also supports government data integration and federated data management by connecting existing sources through a common data model and governed, runtime-generated APIs. Data pipelines—extract, transform, load, and cross-system movement—are configured on a running system without writing code, so supported sources and sensors can be connected through configuration, reducing custom integration work. Data is typed on ingest, synchronized across intermittent links, and released only to entitled consumers under role-based and attribute-aware access control, and agencies keep the systems of record they already operate.
For DevSecOps and infrastructure teams, Guidepad defines services, environments, and deployment plans as structured data. This patented Platform-as-Data architecture provides a consistent foundation for hybrid cloud orchestration across AWS, Microsoft Azure, Google Cloud, and Oracle Cloud Infrastructure, on-premises hardware, and air-gapped AI deployment. Guidepad’s governance continues beyond deployment into runtime: policy is enforced and every operation is audited through a consistent governance model across environments. Teams configure state-based behavior for link loss and degraded conditions, and instances run offline with local inference on appropriately sized hardware, supporting tactical edge and DDIL use cases.
All of this is designed and operated through Canvas View, Guidepad's flagship interface. Canvas View is not a diagram of the system — it is the system. Teams draw services, data flows, workflows, and automations on a live canvas, Guidepad builds and runs what is drawn, and every element shows its real-time state. Operators and analysts extend it without writing code, and organizations arrange canvases by team, functional area, or set of automations, so the enterprise architecture stays current because it is the running system, not documentation of it.
Model lifecycle capabilities extend this governance to model deployment, replacement, and rollback. Shared dashboards bring operational information, model management, and audit views into one interface, helping technical teams and mission owners understand how services are operating.
These capabilities build on production experience. Guidepad developed and deployed an AI-native system for the NSA on SIPRNET (IL6) that has operated in an RMF-accredited classified environment since 2022. Guidepad is currently under contract with the U.S. Army delivering generative AI at the tactical edge. Guidepad is an Army xTech Ignite winner, a Navy Cyber ANTX 2026 winner, and a NAVALX Mid-Atlantic Tech Bridge selection, and holds U.S. Patents 11,907,688 and 12,375,295.
Whether an agency is introducing its first AI-enabled workflow or coordinating services across a complex mission environment, Guidepad provides a reusable foundation for connecting systems, governing execution, and expanding capabilities. Systems integrators can use that foundation to deliver solutions tailored to each customer’s infrastructure and operational requirements.
Optro (formerly AuditBoard), a leading audit, risk, and compliance platform, needed to migrate consulting engagement data to a new professional services system before a year-end license deadline. After a month of effort with existing tools, the legacy data remained unconverted. Using Guidepad's dynamic type system, the REST-to-GraphQL conversion was completed in roughly four hours with minimal custom code. Guidepad then built a downstream pipeline to stream engagement, resource, and hours data into Snowflake for AuditBoard's operational data mart. Reporting was live when the new system launched, and the pipeline continues to run today.
Frequently Asked Questions
Guidepad is a governed runtime for AI agents and federated mission data. Its patented Platform-as-Data architecture defines data types, datastores, adapters, workflows, models, and policies as configurable data, so agencies onboard new systems, build data pipelines, and run ETL at runtime by configuration instead of custom code — and every action an agent takes is authorized, executed, and recorded under policy.
Guidepad deploys identically on AWS, Microsoft Azure, Google Cloud, Oracle Cloud Infrastructure, on-premises hardware, and air-gapped networks, on hardware as small as a Raspberry Pi. Instances operate offline with local inference and signed-token authorization, queue events durably, and resynchronize with the hub when connectivity returns, supporting DDIL operations.
Agents propose actions; operators authorize them; Guidepad executes and records. Every agent runs inside an explicit state machine that bounds what it can do, credentials are scoped per step so the agent never holds them, and every operation is written to a tamper-evident audit archive that can be replayed. Models are swappable through a single model reference — open-weight models served locally on the runtime, or commercial models from the major AI labs accessed through governed connections — with no change to the workflows, policies, or audit around them, so agencies are never locked into one model provider.
Guidepad ships with a FIPS-validated cryptographic mode, CAC/PIV and WebAuthn/FIDO2 authentication, role-based and attribute-aware access control, and audit-by-default logging of every operation and permission decision. Guidepad's developed and deployed an AI-native system for the NSA, that has operated in an RMF-accredited classified environment since 2022.
It doesn't replace anything. Guidepad connects to the applications, databases, and sensors an agency already operates and keeps them as the systems of record; new sources are onboarded by configuration on a running system, typically in hours rather than months, with the resulting APIs, permissions, and audit generated automatically. In one commercial engagement, Guidepad completed a full legacy data conversion into a new platform in about four hours after the customer's data engineering team had spent a month unable to finish it with existing tooling.