Agentic Infrastructure

The Armitive Agentic Framework

A unified AI infrastructure designed for autonomous reasoning, long-term memory, and real-time execution.

GCP Services

Model Backends

Agentic Workflows

99%

Platform Uptime

Explore the Framework GCP Infrastructure

armitive-framework / index.ts

import { ReasoningEngine } from "@armitive/core";
import { MemoryLayer } from "@armitive/memory";
import { InferenceGateway } from "@armitive/inference";
import { EventRouter } from "@armitive/events";

// Assemble the framework
const framework = new AgenticFramework({
  reasoning: new ReasoningEngine({
    strategy: "parallel-then-merge",
    maxDepth: 5,
  }),
  memory: new MemoryLayer({
    backend: "firestore+vector",
    embeddingModel: "text-embedding-004",
  }),
  inference: new InferenceGateway({
    backends: ["gemini-flash", "vertexai", "tflite"],
  }),
  events: new EventRouter({
    transport: "pubsub",
    deadLetter: true,
  }),
});

// Run an agentic workflow
const result = await framework.run({
  objective: "Process incoming resume batch",
  context: { jobId, orgId },
});

Core Pillars

Four layers of the Agentic Framework

Core Pillar 01

Reasoning Engine

Proprietary logic for multi-step agentic task decomposition

The Reasoning Engine is the cognitive core of the Armitive framework. It receives a high-level objective, decomposes it into a dependency-ordered task graph, assigns each node to a specialized sub-agent, and resolves outputs back into a unified result — without human orchestration.

Goal decomposition into directed task graphs
Sub-agent assignment and lifecycle management
Dependency resolution across parallel branches
Retry policies with exponential backoff
Full execution trace per reasoning run
Human-in-the-loop interrupt hooks

armitive / framework / reasoning.ts

// Reasoning Engine — task graph dispatch
const result = await reasoner.run({
  objective: "Score and rank all applicants",
  agents: [ResumeParserAgent, ScoringAgent, RankingAgent],
  strategy: "parallel-then-merge",
});

Core Pillar 02

Memory & Context Layer

Vector databases and Firebase for persistent, context-aware AI

Long-term memory is what separates an agent from a chatbot. The Memory Layer persists interaction context, embeddings, and structured state across sessions using vector search and Firestore — so every agent invocation is informed by full historical context, not just the current prompt.

Vector embeddings for semantic memory retrieval
Firestore for structured persistent state
Session-scoped and long-term memory namespaces
Context window management and summarization
Per-tenant memory isolation
Embedding model: text-embedding-004 (Vertex AI)

armitive / framework / memory.ts

// Memory Layer — semantic context retrieval
const context = await memory.retrieve({
  query: "candidate technical skills",
  topK: 5,
  namespace: `org:${orgId}`,
});

Core Pillar 03

Multi-Modal Integration

Computer vision and NLP through a unified inference abstraction

The inference layer unifies language, vision, and structured data under one API surface. Google Gemini for language tasks and domain-specific models for specialized workloads are both first-class backends — the orchestration layer doesn't need to know which model handles which task.

NLP: Google Gemini (Flash / Pro) for language tasks
Domain-specific models fine-tuned per vertical
Unified inference abstraction — backend-agnostic callers
Vertex AI for managed model training and serving
Structured data and document processing pipelines
Model hot-swap with zero downtime

armitive / framework / multimodal.ts

// Unified inference — backend-agnostic
const output = await infer({
  modality: "vision" | "language",
  model: "auto", // resolved at runtime
  input: payload,
});

Core Pillar 04

Event-Driven Execution

Workflows triggered by real-world signals, not scheduled jobs

Agentic workflows fire in response to real-world events — file uploads, IoT sensor feeds, webhook calls, or database mutations. Cloud Pub/Sub decouples producers from consumers, enabling each platform subsystem to evolve and scale independently.

Cloud Pub/Sub as the asynchronous event backbone
Webhook ingestion from any HTTP-capable source
Document and media upload triggers
Change-data-capture (CDC) triggers on Firestore writes
Dead-letter queuing for unprocessable events
Event replay for deterministic debugging

armitive / framework / events.ts

// Event subscription — Pub/Sub consumer
pubsub.subscribe("document.uploaded", async (event) => {
  await agentPipeline.run(event.payload);
});

Architecture

Request lifecycle — signal to output

Every agentic workflow follows the same deterministic path through the framework.

Signal Ingestion

IoT feed / webhook / file upload enters via Cloud Pub/Sub or HTTP endpoint

Reasoning Engine

Objective decomposed into a task graph; sub-agents spawned per node

Memory Retrieval

Relevant context pulled from vector store and Firestore before execution

Inference Dispatch

Each agent calls the unified inference API — vision or language backend resolved at runtime

State Persistence

Results written to Firestore; embeddings updated in vector store

Output Delivery

Structured result returned to caller; downstream events emitted for dependent workflows

Cloud-Native Scalability

Optimized for Google Cloud Platform

Every layer of the Armitive Agentic Framework is designed around Google Cloud primitives. Cloud Run handles compute with serverless auto-scaling. Vertex AI manages the full model lifecycle from training to online prediction. The result is an infrastructure that scales from zero to production without configuration changes.

Cloud Run

Compute

Serverless containers for all platform services. Auto-scales from zero to hundreds of instances in seconds. Each agent pool runs in an isolated Cloud Run service.

Vertex AI

Model Management

Managed training pipelines, model registry, and online prediction endpoints. Proprietary and foundation models are versioned and served through Vertex.

Cloud Pub/Sub

Event Backbone

Durable, at-least-once message delivery across all platform services. Decouples event producers from consumers for independent scaling and deployment.

Firestore

Persistent State

Document database for structured agent state, user data, and platform configuration. Used as the persistent memory backend with real-time sync capabilities.

Cloud Storage

Artifact Store

Object storage for model artifacts, training datasets, resume files, and exported reports. Lifecycle policies manage retention automatically.

Secret Manager

Secrets

Zero secrets in code or environment variables. All API keys, database credentials, and service account tokens are retrieved at runtime through Secret Manager.

Infrastructure snapshot

gcloud run services list

SERVICE              REGION        STATUS
reasoning-engine     us-central1   ✓ SERVING
memory-layer         us-central1   ✓ SERVING
inference-gateway    us-central1   ✓ SERVING
event-router         us-central1   ✓ SERVING
kavana-api           us-central1   ✓ SERVING
caira-api            us-central1   ✓ SERVING

Serverless — scales to zero between events
Per-service isolation and independent deploys
All secrets via GCP Secret Manager
TLS 1.3 in transit, AES-256 at rest
Cloud Monitoring + Alerting on all services

Framework in Production

Two products. One shared core.

Kavana Studio and Caira are built directly on the Armitive Agentic Framework — each product exercises different subsystems of the same underlying infrastructure.

NLP + Agentic ATS

Gemini inference pipeline for resume scoring
Memory Layer for per-org candidate context
Reasoning Engine for batch scoring workflows
Firestore for pipeline state and RBAC

Explore Kavana Studio

Childcare Documentation AI

Gemini-powered classroom documentation agents
Memory Layer for per-center context and compliance
Event-driven workflow for parent narrative generation
Firestore for structured childcare records

Explore Caira

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Build on the Agentic Framework

The Armitive Agentic Framework is available for enterprise partners and custom implementations. Explore our services or start a conversation.

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