Plan
Clarify intent and produce a governed plan
AI is entering production before organizations have control
Teams are adopting copilots, agents, scripts, model APIs, MCP servers, and internal automations faster than platform teams can govern them. Aintlijx brings identity, permissions, workflows, tools, cost, observability, and audit into one operational control layer.
What you work in
Design partners use one workspace: four studio tabs, plus AI Helper on every screen.
Build
Edit, terminal, and Composer — the only path that applies code changes
Knowledge
Project vaults, search, and cited context
Runs
Inspect what executed, what changed, and what it cost
AI Helper
Explains and suggests on every screen. It does not change your data. Deep insight can research the bound workspace. Code changes stay in Composer, inside Build.
Govern the parts of AI operations that usually sprawl
One operational layer around the primitives production AI actually needs.
Agents
Governed identities with scoped execution
Workflows
Structured, policy-aware automation paths
Tools
Controlled capabilities
Identity
Users, service accounts, and agent principals
Tenancy
Organization and workspace boundaries
RBAC
Product-scoped roles and permissions
Secrets
Vault-backed credential resolution
Cost
Model and capability usage visibility
Audit
Reviewable evidence
Built for teams moving AI into operations
- Platform engineering Governing AI tools, agents, and shared infrastructure
- Product teams Embedding agents into customer workflows with policy boundaries
- Security & compliance Identity, credentials, tenant isolation, and audit evidence
- Operations Replacing manual workflows with governed automation
- Builders Creating multi-product AI platforms on one control foundation
Execution and governance, one layer
Aintlijx sits around agents, workflows, tools, and observability—not as another chat window on top of them.
Platform layers — identity through governed execution.
Governed execution loop
Intent → Plan → Policy → Execute → Observe → Audit
Every meaningful action should be traceable: who requested it, which identity executed it, which tools were used, what policy applied, what it cost, what changed, and what evidence was produced.
Correlation context preserved from trigger to audit.
Example: a governed agent run
A user asks an agent to inspect a project, use approved tools, and produce a result. Aintlijx governs identity, policy, credentials, execution, and evidence—not just the model response.
- Step 1User requests action
- Step 2Agent identity resolved
- Step 3Policy checks run
- Step 4Tools and credentials scoped
- Step 5Workflow executes
- Step 6Trace and audit preserved
A control plane, not another chat window
| Typical AI app | Aintlijx control plane |
|---|---|
| Chat-first UX | Operations-first control layer |
| Fragmented logs after the fact | Correlated traces and audit evidence |
| Shared API keys | Identity-bound tools and credentials |
| Manual review bottlenecks | Policy-aware approval gates |
| Per-app permission silos | Tenant-aware, product-scoped RBAC |
Tenant-aware. Product-scoped. Reviewable evidence.
Aintlijx treats tenancy, product access, environments, secrets, and execution boundaries as first-class platform concerns. Teams can model separation across customers, business units, workspaces, products, and environments, with single-tenant and multi-tenant deployment patterns built into the architecture.
Human users, service accounts, agents, workflows, tools, and product surfaces are governed through scoped identity and policy.
Platform posture
- Kubernetes-native deployment model
- Tenant-aware control boundaries
- Identity-first access design
- Observability and audit from the execution path
- Cost-aware model and capability usage
Product surfaces on the same platform foundation
The customer workspace (Plan, Build, Knowledge, Runs, plus AI Helper) and future product surfaces share the same identity, tenancy, policy, workflow, and audit foundation—not separate permission models per app.