The Agentic Platform
Multi-agent orchestration for autonomous business logic
The shared agentic infrastructure that powers Kavana Studio and amiSense. Built natively on Google Cloud Platform with event-driven triggers, proprietary model inference, and fault-tolerant design — so your workflows run themselves.
2+
Products Live
8+
Agentic Workflows
99%
Platform Uptime
3
Model Backends
Everything required to run production AI at scale
Multi-Agent Orchestration
Coordinate complex agentic workflows without manual glue code
The platform's orchestration layer manages the lifecycle of multiple AI agents running in parallel or sequence. Each agent is scoped, observable, and fault-tolerant — enabling complex multi-step business logic to run autonomously.
- Agent lifecycle management — spawn, monitor, terminate
- Sequential and parallel agent graph execution
- Context propagation across agent boundaries
- Retry policies and exponential backoff built-in
Event-Driven Triggers
React to the world — not just scheduled cron jobs
Workflows fire in response to real-world events: file uploads, API webhooks, database changes, or IoT sensor signals. The event bus decouples producers from consumers so every subsystem can evolve independently.
- Webhook ingestion from any external system
- Database change-data-capture (CDC) triggers
- File-upload and storage event listeners
- IoT sensor signal integration (amiSense feeds)
Proprietary Model Inference
Run your own models alongside best-in-class foundation models
The inference layer serves both proprietary fine-tuned models (EfficientNetV2B0 for amiSense, custom scoring models for Kavana) and Google Gemini calls — through a unified abstraction so the rest of the platform doesn't care which backend handles the request.
- Unified inference API across model backends
- TFLite edge models + Cloud Vertex AI
- Google Gemini integration for LLM tasks
- Model versioning and hot-swap without downtime
Fault-Tolerant Design
Systems that self-heal — so you don't have to
Every platform component is designed to fail gracefully. Circuit breakers prevent cascading failures, queued tasks survive restarts, and the health monitor automatically flags degraded agents for investigation.
- Circuit breaker pattern across all service boundaries
- Durable task queue — tasks survive container restarts
- Health monitoring with auto-restart policies
- Graceful degradation when external APIs are down
Enterprise-Grade Security
Zero-trust architecture from the infrastructure layer up
Security is not a layer on top — it is baked into the platform's architecture. Service-to-service calls are authenticated, secrets are managed through Google Cloud Secret Manager, and all data is encrypted in transit and at rest.
- Zero-trust service mesh — all calls authenticated
- Google Cloud Secret Manager for credentials
- TLS 1.3 in transit, AES-256 at rest
- Role-Based Access Control at the API layer
Built on Google Cloud
Serverless-first infrastructure that scales with demand
All platform services run on Google Cloud Run — serverless containers that scale from zero to hundreds of instances in seconds. Cloud SQL handles relational data, Cloud Storage handles assets, and Pub/Sub is the event backbone.
- Google Cloud Run — serverless, auto-scaling
- Cloud SQL (PostgreSQL) for relational data
- Cloud Storage for assets and model artifacts
- Pub/Sub as the event backbone
From event to outcome — fully autonomous
Event Ingestion
An external event arrives — a file upload, webhook, IoT signal, or scheduled trigger — and enters the event bus.
Workflow Routing
The orchestration layer matches the event to the appropriate agentic workflow and spawns the required agents.
Agent Execution
Agents execute in sequence or parallel, calling inference backends (Gemini, TFLite, Vertex AI) as needed.
Result Propagation
Results are written back to the appropriate data store and downstream events are emitted for dependent workflows.
Observability
Every execution step is traced, logged, and surfaced in Cloud Monitoring so the team has full visibility.
Two products. One shared core.
Kavana Studio and amiSense are independent products — but they run on the same agentic infrastructure, sharing inference, eventing, security, and observability.
AI-Powered ATS
Kavana Studio
Uses the platform's Gemini inference pipeline, event-driven resume ingestion, multi-agent scoring, and Cloud Run deployment to power an end-to-end AI hiring system.
- Gemini LLM inference
- Batch agent processing
- Event-driven pipeline
- Cloud SQL backend
Computer Vision Platform
amiSense
Uses the platform's edge inference layer (TFLite on GCE), IoT event triggers, automation catalog execution engine, and Cloud Storage for model artifact management.
- Edge TFLite inference
- IoT sensor triggers
- Automation catalog
- Model hot-swap
Google Cloud, all the way down
Google Cloud Run
Serverless containers, auto-scale
Cloud SQL / PostgreSQL
Relational data with full isolation
Cloud Pub/Sub
Event backbone, durable queues
Vertex AI + TFLite
Unified model inference layer
Cloud Storage
Assets, model artifacts, exports
Secret Manager
Zero secrets in code or env vars
Build on the Agentic Platform
We're opening early access to the Agentic Platform for select enterprise partners. If your organization runs complex business logic that should be autonomous — let's talk.