Agentic AI coding.
Zero cloud exposure.
AirgapAI Code is a terminal-native autonomous software engineering platform that runs entirely inside your security perimeter. No source code ever leaves your network.
# Analyzing repository structure...
# Found 47 files across 12 modules
@Architect designing auth middleware pattern
@Security reviewing token storage strategy
@QA generating test plan (18 cases)
Editing src/middleware/auth.ts
Editing src/services/token.ts
Editing src/routes/auth.ts
Creating tests/auth.test.ts
Running test suite...
18/18 tests passed
Ready for review // 0 bytes transmitted externally
Not an autocomplete. An autonomous engineering platform.
AirgapAI Code executes the full development loop: understand, plan, modify, run, validate, and commit. It works across your terminal, IDE, and CI/CD pipelines with human-in-the-loop approval gates.
- Single-file snippet suggestions
- Copy/paste workflow
- No command execution
- Minimal governance
- Cloud-mediated processing
- Repository-scale reasoning
- Direct filesystem execution
- Runs builds, tests, git ops
- Enterprise policy controls
- Customer-owned perimeter
Terminal CLI
Full agentic execution in secure shell environments. Scriptable, composable, and pipeline-ready.
VS Code / Codium
Inline diffs, architectural planning, and workspace-aware context directly in your editor.
JetBrains IDEs
Interactive coding and review across IntelliJ, PyCharm, WebStorm, and the full JetBrains family.
Web / VDI
Thin-client browser workflow optimized for virtual desktop fleets and centralized environments.
CI/CD Pipelines
Automated review, remediation, and test execution as part of your existing DevOps workflows.
Purpose-built for secure, autonomous software delivery
From code generation to security review to CI/CD remediation, AirgapAI Code covers the full engineering lifecycle inside your perimeter.
Autonomous Code Development
Takes a natural language objective and executes it across the entire repository: generates code, refactors across files, creates tests, runs build and validation loops, and iterates until acceptance criteria are met.
airgap "implement feature X, write tests, fix failures"Security Analysis
Semantic vulnerability reasoning with exploitability assessment, patch-ready remediation PRs, and human approval checkpoints. Runs fully offline against classified repos.
Git Workflow Automation
Branch creation, selective staging, structured commits, PR/MR creation with summaries and risk notes, and semantic merge conflict resolution.
CI/CD Integration
Plugs into existing git-based workflows. Automates broken pipeline fixes, flaky test remediation, and formatting drift correction.
MCP Connectivity
Connect to on-prem Jira, internal wikis, secure messaging, and custom enterprise systems through the MCP-compatible connector framework.
Governance Layer
Project instruction files, custom commands, lifecycle hooks, model version pinning, and org-level policy packs for enterprise-wide standards.
AIRGAPAI.md + /security-review + hooksParallel specialist agents, not just parallelism
Entourage Mode orchestrates purpose-built sub-agents that bring role clarity, consistent review lenses, and cross-functional engineering rigor without extra meetings.
A virtual engineering team for every task
Each agent applies a specialized lens to your code: architecture, security, quality, and operations. They work in parallel, producing structured outputs that feed back into a unified engineering decision.
@Architect evaluating token exchange patterns...
@Security modeling PKCE + DPoP threat surface...
@QA generating 24 test scenarios...
@DevOps preparing staging environment config...
Interfaces, tradeoffs, dependency choices, and structural patterns.
Secure-by-default patterns, vulnerability triage, and remediation plans.
Test generation, edge cases, regression strategy, and coverage gaps.
Pipeline fixes, deployment scripting, and environment parity checks.
Three layers. All inside your perimeter.
AirgapAI Code is built around customer-controlled model endpoints within the enclave. No external dependencies required.
Developer Surfaces
- CLI agent (terminal)
- VS Code / JetBrains extensions
- CI/CD runner agents
- Web / VDI browser clients
Governance + Orchestration
- Policy engine (RBAC, permissions)
- Audit logging + telemetry
- MCP connector runtime
- SSO / SAML / OIDC integration
On-Prem Inference
- Customer-controlled GPU cluster
- Approved model registry
- Version pinning + update control
- Dedicated security review profiles
Request
User defines task
Plan
Decompose + reason
Execute
Read, edit, run, test
Validate
Tests, lints, scans
Deliver
Diffs, commits, PRs
Security is architectural, not bolted on
AirgapAI Code runs on customer-controlled infrastructure within accredited boundaries, using approved identity, logging, and change control systems.
Complete Data Sovereignty
All code, prompts, and artifacts remain inside your security perimeter. Zero mandatory telemetry. All outbound communications can be disabled by design.
No Training on Your Code
Deployment model completely isolates customer repositories from any external model training pipeline. Your intellectual property stays yours.
Enterprise Identity
SSO via SAML/OIDC, SCIM provisioning, and role-based policy enforcement integrate with your existing identity infrastructure.
Full Auditability
Centralized logs capture every tool action, AI-assisted change, and policy enforcement event. SIEM/SOAR integration ready.
Cloud coding tools vs. AirgapAI Code
The market has proven that agentic coding works. AirgapAI Code removes the adoption blocker for environments that cannot accept cloud data exposure. Which model runs behind the agent is a separate decision, and the ranked coding models roundup works through the current candidates.
Cloud-Based Coding Agents
- Code processed on external cloud infrastructure
- Accreditation path often blocked by egress risk
- Provider-managed model roadmap and versioning
- SaaS-first integration approach
- Security layered onto cloud workflow
- Limited control over data retention
AirgapAI Code
- Code stays inside customer enclave
- Accreditation aligned by architecture
- Customer-approved models with version pinning
- On-prem-first MCP connectors
- Security is inherent: zero external transmission
- Full customer control over retention and logs
What an AI code assistant does in 2026
An AI code assistant reads your repository and writes code with you: inline completion as you type, agentic multi-file edits that run builds and tests, and automated review that flags defects before merge. The difference between products is where your source code is processed while that work happens.
Inline completion
Next-line, whole-function and test-stub suggestions drawn from the file you are in and the symbols around it. The fastest feedback loop, and the one that touches the most code every day.
Agentic edits
A goal instead of a snippet: the assistant plans, edits across files, runs the build and the tests, reads the failures and iterates until the task passes an approval gate you control.
Automated review
Repository-scale reading applied to diffs: dependency and injection risks, missing error paths, dead configuration and style drift, surfaced in the pull request instead of in production.
Where cloud AI code assistants expose source code
Adoption is no longer the open question. Gartner projects that 90% of enterprise software engineers will use AI code assistants by 2028, up from less than 14% in early 2024. Source: Gartner, 2024
The blocker in classified, export-controlled and regulated programs is transport. To answer well, a cloud assistant has to send repository context outward: proprietary algorithms, unreleased architecture, credentials sitting in configuration, and the diff history that shows what a program is about to ship. That egress is what an accreditation boundary exists to prevent, which is why the assistant is often the one productivity tool a secure program cannot approve.
How AirgapAI Code keeps source on the machine
AirgapAI Code runs the model, the agent loop and every tool call on hardware you own. Repository context is read from the local filesystem, inference happens on your GPUs or on the workstation itself, and the only traffic on the wire goes to systems you already operate: your Git server, your pipelines, your internal MCP endpoints.
Disconnected operation is the default rather than a configuration exception, model versions are pinned by your own change control, and your code is never used to train a model. The engineering upside of an agentic assistant arrives without a new egress path to defend.
AI code assistants compared on privacy and deployment
Every product below is strong in the environment it was built for. For a secure program, the column that decides the shortlist is where the code is processed.
| Assistant | Where code is processed | Deployment | Strongest fit |
|---|---|---|---|
| GitHub Copilot | Microsoft-managed cloud | SaaS, with enterprise policy and retention controls | Teams already standardized on GitHub and Visual Studio tooling |
| Cursor | Cloud inference called from the editor; privacy mode limits retention | SaaS | Fast agentic editing for product teams on connected networks |
| Claude Code | Cloud model APIs, including enterprise routes through Amazon Bedrock and Google Vertex AI | Local CLI against hosted models | Terminal-native agentic work with repository-scale reasoning |
| Amazon Q Developer | AWS-managed cloud | SaaS inside the AWS account boundary | AWS-centric estates that want assistant and platform on one contract |
| Tabnine | Cloud, or infrastructure the customer owns on its private tier | SaaS, VPC or self-hosted | Enterprises that want a private deployment without assembling one |
| Open-source clients with local models | The developer workstation or an on-prem inference server | Self-assembled | Teams with the platform engineering capacity to run and support models |
| AirgapAI Code | Your own machines and enclave, with no external transmission | On-prem, VDI, and fully disconnected environments | Defense, critical infrastructure and regulated enterprise programs |
For a ranked view of the private options, see the self-hosted AI coding assistants comparison. Which model runs behind the assistant is a separate decision: the best LLM for coding roundup ranks the current candidates and the LLM selection guide works through the open-weight and commercial trade-offs. AirgapAI Code sits alongside the rest of the AirgapAI platform, so the same enclave serves engineering and the wider workforce.
Deployment Options
On-Prem Data Center
Centralized inference cluster with distributed IDE and CLI clients across the enterprise.
Private Cloud
Deploy into your approved VPC or VNET for organizations standardizing on internal cloud platforms.
Air-Gapped / Disconnected
Offline packages with signed updates for classified and mission-disconnected networks.
VDI / CI/CD Agents
Virtual desktop integration and pipeline automation for centralized engineering operations.
ROI Framework
Cycle Time Reduction
Faster implementation and iteration through autonomous task execution.
Rework Reduction
Fewer defects through automated testing and pre-commit validation loops.
Throughput Increase
More PRs shipped per developer without proportional headcount growth.
Time to Payback
Payback measured in weeks, not quarters, when deployed broadly across engineering.
Tools without training stall. Train the team that runs it.
Cloud-grade autonomous coding. Inside your security boundary.
AirgapAI Code delivers the productivity upside of agentic development while meeting the operational reality of defense, critical infrastructure, and regulated enterprise environments.