The layer that keeps it running at 3am.
iLeaf runs the infrastructure under the systems we build: cloud migration, CI/CD, observability and incident response across AWS, Azure and Google Cloud. For AI workloads we add AgentOps — runtime enforcement of policy, spend and latency for every agent, with escalation to a human instead of an unattended retry.
Ask Lia about cloud & agentopsWhat this includes
- Cloud migration
- Moving live systems between environments with a rehearsed cutover and a tested path back, not a weekend and hope.
- CI/CD pipelines
- Build, test and deploy automation with environment parity, so a release is routine rather than an event.
- Observability
- Traces, metrics and logs wired to the questions you will actually ask during an incident.
- AgentOps
- Per-agent budgets, tool scopes, approval gates and full action audit — the operational half of shipping AI.
- MLOps
- Model and prompt versioning, evaluation gates in the pipeline, and rollback that does not require a redeploy.
- Cost engineering
- Right-sizing, caching and model routing. On AI workloads the token bill is usually the surprise, so we meter it from day one.
How we run it
Every phase ships something usable on its own, so you are never holding a half-finished system waiting on the next milestone.
Measure before moving anything
Current cost, latency and failure modes get baselined first, so improvement is demonstrable rather than asserted.
Make deploys boring
Automated checks, one-command rollback and identical environments come before any migration work begins.
Instrument the agents
Every agent run emits cost, tokens, tool calls, latency and outcome. You cannot govern what you cannot see.
Rehearse the failure
We test the rollback and the escalation path deliberately, before production does it for us unannounced.
What we build it with
- AWS
- Azure
- Google Cloud
- Terraform
- Docker
- Kubernetes
- GitHub Actions
- OpenTelemetry
- Grafana
- Prometheus
- Redis
- On-premise GPU
Questions we get asked
What exactly is AgentOps?
AgentOps is the operational layer around AI agents in production. It enforces what an agent may do, how much it may spend and how long it may take, records every action with its reasoning, and escalates to a human when a limit is hit or confidence is low. It is the difference between a demo and a system you can be accountable for.
Can you work with our existing cloud setup?
Yes, and we prefer to. We start with an audit of what you run and how it deploys, then improve the parts that hurt most — usually release safety and observability. Wholesale re-platforming is only worth it when the current setup blocks something you need, and we will say so plainly.
How do you stop AI costs running away?
Per-agent and per-tenant budgets enforced at runtime, caching for repeated retrieval, and routing cheaper models to the tasks that do not need a frontier model. Spend is on a dashboard from the first day, so a cost problem is visible in hours rather than at the end of the month.
Let’s talk about cloud & agentops.
Tell us what you are running and what it needs to do next. We will tell you honestly whether we are the right team for it.