Speed
Give teams agent capacity for bug fixes, tests, documentation, internal tools, and operational tasks without waiting for a separate platform build.
GTA Limited / Private agentic engineering
Let engineering teams use AI agents without losing control.
Private, governed AI workspaces for enterprise teams that cannot put code, data, secrets, or operations into uncontrolled SaaS.
Executive case
Make agentic engineering useful, governable, and visible.
AI agents are moving into software delivery faster than most organisations can operationalise them. GTA provides a controlled environment where engineering teams can delegate real work without losing command of source code, infrastructure, approvals, or evidence.
Control
Define where agents run, what they can access, which tools they can use, and which actions require human approval.
Proof
Capture logs, diffs, browser evidence, test output, decisions, and action history so agent work can be reviewed and trusted.
How it works
A governed path from task request to approved change.
- TaskEngineering request
- RuntimeIsolated workspace
- AgentTool-bounded execution
- EvidenceTests and browser checks
- ReviewHuman approval
- OutputPR or deployment
- RecordAudit history
Platform
Built for enterprise engineering systems, not casual prototypes.
GTA Agent Workspaces are designed to fit the tools technical teams already use: repositories, review flows, CI/CD, containers, browser testing, observability, and deployment environments.
Explore platform capabilitiesPrivate repos
Connect controlled source repositories without forcing teams into a new development model.
Runtime isolation
Run agents in bounded workspaces with explicit network, filesystem, and tool access.
Tool allowlists
Expose only the commands, browsers, APIs, and integrations approved for the task.
Secrets boundaries
Keep credentials scoped, masked, and separated from model-visible task context.
Browser automation
Let agents inspect, test, and verify real application behaviour in controlled browsers.
GPU workloads
Support heavier AI, media, and data workloads where dedicated compute is justified.
Deployment hosting
Host preview apps, internal tools, APIs, scheduled jobs, and production services.
Operational evidence
Record logs, screenshots, test output, build history, and reviewer decisions.
Governance plane
- SSO, role-based access, and workspace permissions
- Approval gates for PRs, deployments, and sensitive tools
- Policy controls for networks, commands, and integrations
- Audit logs for agent actions, human decisions, and outputs
- Rollback paths for code, services, and environment state
Governance
The safety layer is the product.
Enterprise buyers will not adopt agentic engineering because an agent can write code. They will adopt it when the organisation can define boundaries, preserve evidence, control risk, and explain exactly what happened.
Review governance controlsArchitecture
A private execution plane for agent work.
The architecture separates the user request, policy layer, agent runtime, tool gateway, service containers, data stores, deployment edge, and observability stream.
Open technical architecture
User and source control
Policy and approval layer
Agent runtime
Tool gateway
Service containers
Data stores
Deployment edge
Observability
Pilot offer
Start with one private repo and one high-value workflow.
The recommended first engagement is a controlled technical pilot: connect one repository, select one workflow, run agent work in an isolated environment, and measure speed, review quality, policy fit, and developer acceptance.
Plan an enterprise pilotContact
Discuss private agent workspaces for your engineering organisation.
GTA Limited is building for enterprise teams that need AI agent capability inside private, accountable, technically serious infrastructure.
brett@gt-a.uk