Build, govern, and scale enterprise AI agents and AI applications.
Gaia is the governed platform for building, operating, and scaling enterprise AI agents and AI applications. It gives platform and delivery teams one operating model for runtime control, evaluation, delivery control, and lifecycle evidence.
Gaia is the governed platform for building, operating, and scaling enterprise AI agents and AI applications.
Gaia turns agents into an operable enterprise capability by connecting solution design, runtime orchestration, shared document work, and operational governance.
- Use one governed platform to design, orchestrate, and scale enterprise AI agents and AI applications.
- Apply AI governance and risk controls with policy enforcement, traceability, and access control.
- Run agents, workflows, evals, and delivery evidence inside one control plane instead of rebuilding across separate AI tools.
- Operate across teams, clouds, and model providers without rebuilding controls around every new agent system.
Agent builders and frameworks
They help teams stand up agents quickly, but the operating model, controls, and integration layer still have to be assembled around them.
- • Fast agent creation still leaves high integration overhead per project
- • Controls and evidence are standardized only after delivery starts
Copilot / no-code builders
Fast for narrow workflows, with limited enterprise operating control.
- • Harder to extend into complex multi-step processes
- • Governance, evals, and release discipline sit outside the builder
Stitched enterprise AI stacks
Separate copilots, search, workflow tools, evals, dashboards, and governance records create an integration program instead of one operating model.
- • Duplicated integrations and recurring glue work
- • Fragmented controls, logs, and delivery evidence
- • Unclear ownership across design, runtime, and release
- • Each new use case reopens architecture decisions
Six work surfaces make the platform concrete.
Gaia carries the work itself, not just the controls around it. These public entry points show where agent operations, drafting, evidence, evaluation, and delivery happen in practice.
Governance is now a visible product surface, not an implied promise.
The user guide now documents the Governance workspace as a concrete operating module with linked controls, obligations, classifications, discovery, operations, and explainability workflows.
Close the gap between design intent and production operations.
Design-time policies and constraints carry through runtime behavior and change decisions.
Coordinate agents, tools, models, and evidence through explicit control points.
ModelOps-style monitoring and evaluation signals guide release readiness and evolution.
The three pillars
Governance, collaboration, and ModelOps-style operations — embedded by design.
GLASSBOX
AI governance and risk controls are inspectable in day-to-day operations.
CO-DEVELOPMENT
Business, risk, and engineering teams work from a shared view of decisions and evidence.
MODELOPS DISCIPLINE
Evaluation, monitoring, and release decisions follow a repeatable operational cadence.
Core control layers
Shared lifecycle capabilities across design, execution, and evolution.
Agent and workflow orchestration
Coordinate enterprise agents with policy-aware routing, execution flows, and handoffs.
Model & tool execution
Run multi-model, graph-based workflows with controlled tool invocation and observable outcomes.
Data & context access
Provide governed access to enterprise data, document folders, and contextual knowledge.
User interaction layers
Support web, API, and multimodal channels with consistent control policies.
Evaluation & quality management
ModelOps-style operational controls for evaluation, regression checks, and readiness.
Auditability & access control
AI TRiSM-aligned logging, role-based access, and policy traceability.
A complete operating architecture for enterprise AI agents and AI applications.
Execution, knowledge, and governance layers work together so agents and AI applications behave as long-lived enterprise operational systems.
Operational lifecycle built into the platform
Governed design artifacts
Capture intent, policy, and constraints as first-class operational assets.
Controlled runtime operations
Runtime orchestration and tool usage stay within defined policy boundaries.
Continuous evaluation and monitoring
Quality, safety, and performance signals are measured on an ongoing basis.
Managed release and evolution
Operate, audit, and evolve AI agents and AI applications with controlled change decisions.
Multi-cloud operations with multi-model choice.
Centralize observability across Azure, Google Cloud, AWS, and Oracle Cloud while orchestrating OpenAI, Claude, Gemini, Mistral, and other model providers.
Microsoft Azure
Google Cloud
Amazon AWS
Oracle OCI
OpenAI
Claude
Gemini
MistralFor teams operating AI as core infrastructure.
Gaia is designed for enterprises that need repeatable governance and risk controls for agent operations, operational stability, and long-term system evolution.
- CIO, CTO, and platform leaders standardizing agent operations across business units.
- Teams running high-impact AI workloads in regulated or risk-sensitive environments.
- Organizations moving from agent pilots to governed enterprise AI application portfolios.
Platform overview
See how Gaia connects design intent, runtime orchestration, evaluation, delivery, and governance.
Explore platformApplications
Browse six reference application types and the shared work surfaces they inherit.
Browse applicationsDelivery process
Review the four-stage operating rhythm for planning, evidence capture, and release readiness.
Review processResources
Open the user guide, handbook, and public docs before deciding whether you need a live walkthrough.
Open resourcesRun enterprise AI agents and AI applications with operational confidence.
Gaia unifies governance, cross-team delivery, and lifecycle operations for enterprise AI agents and AI applications.