Oahu
Agent platform · the foundation Castro, Vegas, San Francisco, and Peet's run on
The foundational platform for designing, deploying, and governing autonomous AI agent ecosystems. Oahu provides the operating layer on which all GIL agent applications run — a unified cognitive architecture for continuously evolving agent systems.
The Architecture
Three Layers. One Ecosystem.
Oahu structures agent ecosystems into three interconnected layers — each responsible for a distinct aspect of autonomous operation. Together, they create a self-governing system where agents can be designed, deployed, and managed at any scale.
Design
Agent Definition & Configuration
Deploy
Runtime & Orchestration
Govern
Policy, Rules & Oversight
Design Layer
Agent DefinitionDefine agents by their objectives, capabilities, constraints, and interaction patterns. The design layer provides a declarative framework for specifying what an agent should accomplish, what resources it can access, and how it communicates with other agents in the ecosystem.
Deploy Layer
Runtime & OrchestrationInstantiate, scale, and orchestrate agents in real time. The deploy layer manages agent lifecycles, handles resource allocation, coordinates inter-agent communication, and ensures the ecosystem adapts to changing workloads — from a single agent to thousands operating in concert.
Govern Layer
Policy & OversightEnforce rules, monitor behavior, and maintain alignment. The governance layer defines operational boundaries, tracks agent performance against objectives, detects anomalies, and provides the audit trail needed for transparent, accountable AI operations at scale.
Platform Capabilities
The Operating System for Agent Intelligence
Oahu is the foundation on which GIL's specialized agent applications — Castro, San Francisco, Vegas, and Peet's — are built. It provides the shared infrastructure that makes each application possible.
Declarative Agent Design
Define agents through objectives and constraints rather than procedural code. Specify what an agent should achieve, and Oahu determines how to execute it within the ecosystem.
Continuous Evolution
Agent ecosystems are not static. Oahu supports live reconfiguration — agents can be added, removed, or modified without disrupting the running system.
Observability & Audit
Every agent action, decision, and inter-agent communication is observable. Full audit trails provide the transparency required for governance, debugging, and continuous improvement.
Policy-Driven Governance
Operational rules are defined as policies, not hardcoded logic. Governance adapts as the ecosystem evolves — ensuring agents stay aligned with organizational objectives and ethical boundaries.
Application Architecture
Five Applications. One Platform.
Oahu is the shared operating layer. On top of it, four specialized agent applications handle distinct workflow patterns — from linear pipelines to collaborative rooms. Each application leverages Oahu's design, deploy, and govern layers while solving a different class of problem.
Castro
Idea-to-delivery pipeline with structured agent handoffs.
Vegas
Continuous review-create-execute loop for rapid iteration.
San Francisco
Dynamic agent orchestration and deployment at scale.
Peet's
Multi-agent collaborative rooms with real-time role management.
Design
Agent definition, objectives, constraints
Deploy
Lifecycle, scaling, communication
Govern
Policy, audit, alignment
Sequential vs. Cyclical
Castro runs a one-directional pipeline — concept flows through to delivery. Vegas runs a loop — outputs feed back into the next cycle. Both use Oahu's handoff and validation infrastructure.
Orchestration vs. Collaboration
San Francisco coordinates agents through assignment — matching capabilities to work. Peet's puts agents in shared rooms where they interact directly. Both rely on Oahu's deploy and govern layers.
Built on Oahu
Specialized agent applications that run on the Oahu platform.
The Foundation for Agent Intelligence
Oahu powers GIL's entire agent application suite — providing the shared infrastructure for design, deployment, and governance of autonomous AI systems.
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