Control of the estate
A live agent registry showing every active agent, the models it runs on, and the systems it may touch. Shadow AI stops being invisible.
Endeavor for CIOs and CTOs
Endeavor gives technology leaders one governed control plane over every AI agent in the organisation. You are accountable for AI outcomes, but the evidence sits in technical consoles your business stakeholders cannot read — and much of the activity never reaches a console at all.
The problem is not capability. It is control.
Every platform in your stack ships an AI assistant with its own console, its own permissions model, and no view of the others. Meanwhile your teams close the gaps with custom wrappers and one-off integrations that accumulate into governance debt nobody has time to service.
Instead of managing fifty AI configurations, you manage one instance that governs many agents.
What you get
Proof of spend
Real-time token visibility at agent and task level, not just the subscription line, so you can answer what AI costs and what it returned per workflow.
Sovereignty you keep
Private Kubernetes namespace, bring your own keys, egress restricted to approved endpoints. Your data stays in your jurisdiction.
Separation of duties that actually holds
IT governs the infrastructure
Kubernetes, Postgres, secrets, network isolation, and the global guardrails every agent inherits. A standard security baseline applied once, not per project.
The business governs the logic
Domain experts own their agents' instructions, test cases, and success metrics, so you stop being the bottleneck for every prompt change.
Security gets a forensic trail
Reasoning-level logs and an immutable record of instruction changes turn an incident review into minutes rather than a fortnight of log gathering.
No rip and replace
Endeavor sits over AWS, Azure, and Google Cloud rather than competing with them. Existing investments keep working.
What it takes to run
| Component | Requirement |
|---|---|
| Kubernetes cluster | Three worker nodes minimum for high availability, general-purpose instances |
| PostgreSQL | Managed instance recommended, with provisioned IOPS for responsiveness |
| MCP server | A containerised microservice inside the same cluster |
| GPUs | Not required unless you choose to host models locally |
| Network | Dedicated VPC, no direct public exposure, egress limited to approved endpoints |
What stops landing on your desk
- Requests to hand-build another one-off integration between two systems.
- Arguments about whether an AI error came from the data, the prompt, or the model.
- Business units queuing for IT to approve every prompt revision.
- Discovering an agent running on a service account nobody can attribute.
- Explaining AI spend to finance without per-workflow attribution.
Frequently asked questions
We already have Azure AI Foundry and Copilot. Why add Endeavor?
Those consoles are built for developers and IT admins and are scoped to their own ecosystem. Endeavor is the layer above them: it governs agents that need data to move across multiple platforms and legacy systems, and gives business owners visibility they cannot get from a technical console.
How does this reduce governance debt rather than add to it?
Every bespoke AI wrapper your teams build becomes something to maintain and secure separately. Endeavor replaces many one-off configurations with one governed instance, so a security baseline is applied once rather than per project.
Can we see what AI actually costs per workflow?
Yes. Token spend is visible at agent and task level rather than only at the subscription line, which is what makes per-workflow ROI calculable.
What happens to agents when an employee leaves?
Agent ownership and permissioning are centralised, so instructions and keys created by a departing user are revoked in one place rather than left running unattributed.