Services · AI & Agentic Operations
AI that operates your infrastructure — and answers to your policies
We design and run agentic systems that read your telemetry, reason over your runbooks and act through GitOps. Every action is scoped, policy-checked and reversible.
The problem
Why this matters now
Your observability stack produces more signals than any on-call rotation can read. Alerts fire, engineers context-switch through five dashboards, and the fix is usually a rollback someone could have predicted. The bottleneck isn't data. It's the time between signal and action.
What we deliver
What we offer
AIOps Incident Triage & Self-Healing
Real-time correlation across metrics, logs, traces and deploy history. Bounded remediations — rollback, scale, restart, cordon — executed under policy.
- Prometheus
- Loki
- Tempo
- Pyroscope
- Alertmanager
- Argo Rollouts
CI/CD & IaC Agents
Agents that review every Terraform plan and Helm diff for cost, blast radius and policy drift, and comment on the PR before a human does.
- GitHub Actions
- Atlantis
- OpenTofu
- Checkov
- Conftest
MLOps & LLM Infrastructure
GPU node pools on Kubernetes for inference and fine-tuning, with autoscaling, model registries and cost controls.
- vLLM
- Ray
- Triton Inference Server
- KServe
- NVIDIA GPU Operator
- Karpenter
Agentic Workflow Integration
LLMs connected to your infrastructure APIs through scoped, audited tools — no shared admin tokens, no hidden side effects.
- MCP
- LLM tool-calling
- AWS Bedrock
- Azure OpenAI
- Self-hosted models
- OPA
Architecture
How our agentic system works
- 1
Signals
Prometheus · Loki · Tempo · K8s events · Hubble
Alertmanager, Loki rules, Kubernetes events and eBPF flow data (Cilium Hubble, Tetragon) are routed through an event bus (Argo Events or NATS). Nothing new to install if you already run the standard stack.
- 2
Context
Topology · ArgoCD history · Backstage · Runbooks
Before the model sees anything, we assemble context: service topology, the last deploys from ArgoCD, ownership from Backstage, and the matching runbooks retrieved from your own documentation.
- 3
Reasoning
vLLM in your VPC · AWS Bedrock · Azure OpenAI
The agent forms hypotheses and tests them with queries — PromQL, LogQL, TraceQL. It runs on the model you choose. Your telemetry never has to leave your boundary.
- 4
Tools
MCP servers · typed schemas · scoped accounts
The agent acts only through MCP tools we define with you. Each tool has a typed schema, a scoped service account and rate limits. The default set is read-only.
- 5
Policy gate
Kyverno · OPA · autonomy level · blast radius
Every proposed action is checked against policy-as-code: autonomy level, service tier, change windows, blast radius. Kyverno and OPA decide — not the model.
- 6
Execute
Git PR → ArgoCD / Flux · bounded runtime ops
State changes go through Git: a pull request that ArgoCD or Flux reconciles. Time-critical operations (rollback, scale-out, pod restart) run through a narrow, pre-approved runtime path and are written back to Git.
- 7
Verify
SLO burn rate · automatic revert
The agent checks its own work against SLOs. If the burn rate doesn't drop, it reverts and escalates to a human with everything it found.
- 8
Learn
Postmortem draft · runbook updates
Each incident produces a postmortem draft and proposed runbook updates — reviewed by your engineers before they become context for the next incident.
- Every step is written to an immutable audit trail: prompt, context, tool calls, decision and outcome.
Autonomy levels
You choose the level. Policy enforces it.
| Level | Name | The agent | Your engineers |
|---|---|---|---|
| L0 | Observe | Summarises alerts and correlates signals | Does everything |
| L1 | Advise | Proposes a diagnosis and an action | Executes |
| L2 | Propose | Opens the pull request with the change | Approves and merges |
| L3 | Act within guardrails | Runs whitelisted actions and verifies them | Reviews afterwards |
| L4 | Autonomous | Remediates and closes known incidents | Audits by sampling |
- No standing admin credentials — short-lived, scoped tokens per tool
- Prompt-injection defences on every log and ticket the agent reads (OWASP LLM01)
- Human approval required above your chosen autonomy level
- Full audit trail: prompt, context, tool calls, decision, outcome
- Kill switch: one flag returns every agent to L0
Outcomes
What changes for your team
- Lower MTTR for known incident types
- Fewer pages reaching a human at 3 a.m.
- Every agent action reviewable in Git
- Telemetry that never leaves your boundary
Start where it's safe
Our assessment identifies the first three incident types an agent can handle in your environment — and the guardrails it needs.
- Kubernetes & security posture
- Observability & SLO coverage
- CI/CD & IaC governance
- Where agents can safely act first