AAGP is not cloud-portable. It is designed from the ground up around Vertex AI, Gemini, and GCP's native capabilities for governed multi-agent AI. Every Google Cloud improvement directly improves our platform.
This is not a "preferred cloud" arrangement. GCP provides specific capabilities our architecture depends on — capabilities that don't exist together on any other platform.
Gemini isn't a model we call through an API. It's the orchestration layer of our consensus architecture — leading deliberation, synthesizing perspectives, and generating governance verdicts. Native Vertex AI access gives us the context window, grounding capabilities, and latency characteristics this role requires.
AAGP is a system of specialized AI agents that collaborate, validate each other, and reach consensus before any action is taken. Vertex AI Agent Builder provides the native orchestration we need — we build governance on top, not from scratch.
Multi-model verification requires access to models with different architectures through a unified interface. Model Garden provides this — multiple frontier models accessible through one API, managed under one billing and governance framework.
Our first product (AiRadics) depends on Google Ads API for campaign execution. GCP provides first-party access with the lowest latency and tightest integration — a real operational advantage for our dental marketing vertical.
Our roadmap includes healthcare verticals with HIPAA requirements. GCP provides BAA support, built-in compliance controls, and healthcare-specific certifications that make this expansion possible without re-platforming.
We're building on Google's Agent Development Kit (ADK) roadmap intentionally — not reactively. Aligning our multi-agent governance architecture with Google's platform investments early, before architectural decisions become hard to reverse.
Architecture finalized. Active development begins March 2026. Each service was chosen for a specific role in the AAGP governance pipeline.
Gemini leads the multi-model quorum. Vertex AI provides agent orchestration, embeddings, and Model Garden access. This is the brain of the entire platform.
Multi-agent system orchestration. Specialized agents collaborate, validate each other, and reach consensus. We build governance controls on top of native capabilities.
Every governance decision produces audit records — inputs, model versions, compliance rules, consensus results, outputs. BigQuery stores and enables replay of the complete decision provenance.
Regulations compiled into searchable vector embeddings. When an AI agent proposes an action, compliance rules are matched semantically — not just keyword lookup.
Serverless compute for the governance pipeline. Auto-scales based on decision volume. Each governance check runs as an independent, observable service.
Asynchronous event-driven communication between agents and governance gates. Enables real-time monitoring, circuit breaking, and parallel processing of multi-model deliberations.
A conceptual view of the AAGP pipeline — from data ingestion through governed AI processing to human-approved execution.
Market signals, practice data, competitive intelligence collected and normalized
Specialized agents analyze data, generate recommendations under governance constraints
Multi-layered safety checks: compliance, validation, multi-model consensus, verification
Governed recommendations presented with full reasoning. Human reviews, adjusts, approves
Approved actions executed across channels. Complete decision trail cryptographically sealed
Every step produces audit records stored in BigQuery. The complete pipeline is deterministically replayable — a core requirement for regulated deployment. Agent decomposition and service mapping are being finalized ahead of development start in March 2026.
AAGP is a system of specialized AI agents — not a single model. These principles govern how agents interact, regardless of final decomposition.
Multiple AI models with different architectures must agree on recommendations before any action proceeds. Disagreement triggers structured resolution — not silent override. No single model decides alone.
Each agent validates outputs from previous stages. Errors are caught early in the pipeline — not discovered after damage is done. Every handoff between agents produces verifiable records.
AI recommends. Humans approve. The system executes only what's been explicitly authorized. Full reasoning is presented with every recommendation so approval is informed, not blind.
Agent decomposition and GCP service mapping are finalized at the design level. Active development on Vertex AI begins March 2026. These orchestration principles are patent-protected — covered by 12 provisional patents filed December 2025.
No AWS or Azure fallback. No multi-cloud hedging. Our architecture depends on GCP-specific capabilities that don't exist elsewhere in a single platform.
Happy to be a case study for governed AI agents on Google Cloud. Our use case — multi-model consensus in regulated industries — is a compelling story for the GCP ecosystem.
177K+ dental practices in the U.S. alone, with expansion into adjacent healthcare and regulated industries. Every customer we add means more Vertex AI, BigQuery, and Cloud Run consumption.
Explore the product, the Gemini-led governance architecture, or the patent portfolio that protects our IP.