Operate inside the boundary
Keep agent orchestration, tool use, data access, and model calls within customer-controlled infrastructure—without a public model API or external SaaS control plane at runtime.
Open source · Inference choice · Forward-deployed engineering
Air-gapped agentic AI for
defense and intelligence environments.
Decision Terrain implements air-gapped agentic AI on an open-source platform connected to the inference stack your organization selects. Forward-deployed engineering (FDE) adapts it to mission systems and controls inside the environment.
Open-source agent foundation
Your inference stack
Forward-deployed implementation
Specialist consulting and implementation. This service assesses and implements a scoped architecture for the customer’s environment, which may use DT or another platform. It does not establish that every DT platform capability operates offline.
Review DT platform deployment options01 / Operating requirement
The capability must fit the boundary—not ask the boundary to move.
Many agent platforms assume access to hosted models, public APIs, external telemetry, and vendor-managed services. Those assumptions do not carry into disconnected or tightly controlled environments.
Air-gapped agentic AI needs a different implementation model. The runtime operates with local inference, while each tool and workflow is engineered for the systems and authorities inside the boundary. Our air-gapped agentic AI architecture reference shows how those layers and trust boundaries fit together.
Use the inference you approve
Connect the platform to the organization’s selected on-premises or enclave-accessible inference stack instead of forcing the mission into a predetermined model provider.
Fit the systems already there
Integrate agents with approved data, tools, identity, logging, policy, and human-review points so the capability reflects the environment in which it must operate.
Leave a maintainable capability
Build the deployment, documentation, evaluation practices, and knowledge transfer required for the customer team to understand and operate what has been delivered.
02 / Solution model
An open foundation, engineered for your environment.
This is not a cloud product repackaged for an enclave. Decision Terrain deploys the platform inside the customer environment, connects approved inference, and builds a bounded workflow that can be tested where it will operate. Use our guide toevaluate an open-source agent platform for an air gap before the technical foundation becomes difficult to replace.
Discuss the target environmentOpen-source agent platform
Establish the agent runtime, orchestration patterns, tool interfaces, and deployment baseline on an open foundation that can be inspected and adapted.
Starting output: a deployable agent-platform baseline inside the target environment.
Customer-selected inference
Integrate the platform with the inference service, model runtime, or accelerator stack the organization has selected and approved.
Starting output: working model access with explicit performance and operating constraints.
Mission workflow integration
Connect agents to the local tools and data required for a defined workflow, with clear boundaries around permissions, actions, and human oversight.
Starting output: a bounded workflow that can be exercised and evaluated in context.
Forward-deployed engineering
Work alongside mission, platform, and security teams to resolve deployment friction, adapt integrations, test behavior, and transfer operational knowledge.
Starting output: a supported path from initial deployment to customer operation.
03 / Forward-deployed engineering
Implementation happens with the teams who own the environment.
Forward-deployed engineering keeps product and integration work close to the mission. Decision Terrain works with the customer’s technical, mission, and security teams to resolve constraints and transfer operational knowledge. The companion guide explains theforward-deployed engineering delivery model for defense AI in greater detail.
Define the boundary and workflow
Identify the user, decision, data, allowed actions, infrastructure, inference options, and operating constraints before selecting an implementation path.
Establish the in-environment baseline
Deploy the open-source agent platform and connect it to the chosen inference layer using components suitable for the target environment.
Integrate and evaluate
Add the smallest useful set of tools and data, then test task performance, failure modes, permissions, observability, and human-control points.
Operationalize and transfer
Document the architecture, automate repeatable operations, train the customer team, and define the support model for continued improvement.
04 / Delivery principles
Customer control is an architectural requirement.
The implementation is designed around portability, inspectability, and a clear transfer of responsibility—not dependence on Decision Terrain as a permanent external control plane. That responsibility includes a documented, reversibleair-gapped AI model update lifecycle for models, runtimes, and dependencies.
Open foundation
The implementation is based on an open-source agent platform rather than a proprietary control plane that the customer cannot inspect or operate.
Inference choice stays yours
Model and runtime decisions follow the mission, infrastructure, and evaluation evidence—not a required commercial inference provider.
Deployment is part of the work
The engagement addresses packaging, integration, evaluation, documentation, and operational handoff—not only an out-of-environment demonstration.
Customer authorities remain authoritative
Decision Terrain engineers to the organization’s requirements; customer platform, security, and authorizing teams retain their respective decisions and authorities.
05 / Practical questions
What teams usually need to know first.
A credible starting scope depends on the target boundary, available infrastructure, selected inference, and one defined workflow. Those facts matter more than a generic feature list.
What does air-gapped mean in this solution?
The target capability is designed to operate without public model APIs or an external SaaS control plane at runtime. The exact architecture depends on the boundary, infrastructure, and components the customer approves.
Which models and inference systems can it use?
The organization’s inference of choice, provided the selected runtime exposes an interface that can be integrated and meets the environment’s technical and operating requirements.
Does this engagement require the DT platform?
No. DT supports air-gapped deployment, and this independent consulting engagement can also use another selected open-source platform. We define the configuration and test the intended workflow against the customer’s requirements. Platform selection follows those requirements and the evidence.
What does forward-deployed engineering include?
FDE can include architecture, deployment automation, inference integration, agent and tool development, evaluation, troubleshooting, operational documentation, knowledge transfer, and ongoing engineering support.
06 / Start a conversation
Bring the mission and boundary.
We’ll define a credible first deployment.
At an unclassified and non-sensitive level, describe the target environment, candidate inference, and one workflow. We’ll identify a useful first scope and the FDE support it requires.
Discuss an air-gapped deployment