Active2024-2025

VertiCore

Enterprise Agent Architecture Framework

Agentic AILangGraphTemporalEnterprise SecurityWorkflow Orchestration

Code Availability

This is a proprietary research project. Source code can be shared for academic and research purposes upon request.

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Overview

VertiCore is an enterprise-grade agentic AI architecture designed for production deployments where reliability, security, and compliance are non-negotiable. The framework addresses the critical gap between experimental AI agents and enterprise-ready autonomous systems.

The architecture uniquely combines LangGraph for sophisticated reasoning and tool orchestration with Temporal for bullet-proof workflow durability. This hybrid approach enables agents that can reason through complex multi-step tasks while guaranteeing exactly-once execution semantics, automatic retries, and complete audit trails.

Reasoning Framework Optionality

VertiCore decouples the reasoning engine from the execution runtime. While LangGraph is the primary driver for agentic loops, the architecture supports pluggable reasoning modules. This allows teams to switch between different cognitive architectures (e.g., ReAct, Plan-and-Solve, or custom state machines) without rewriting the underlying orchestration logic. The 'Intelligence Plane' handles the cognitive load, while the 'Control Plane' enforces policy.

The Hybrid Execution Model

A core innovation of VertiCore is its hybrid execution model. Standard agent frameworks often fail in production due to transient errors or context window limits. VertiCore wraps LangGraph nodes within Temporal activities. This means every step of the agent's reasoning—every tool call, every LLM thought—is a durable, checkpointed event. If an agent crashes mid-thought, it resumes exactly where it left off, ensuring zero data loss and high reliability.

Security & Governance

Security is treated as a first-class citizen, not an afterthought. The architecture implements a 'PII Vault' pattern where sensitive data is redacted before entering the LLM context and rehydrated only at the final output stage. A granular policy engine enforces role-based access control (RBAC) on tool execution, ensuring that agents can only perform actions they are explicitly authorized to do. Human-in-the-loop gates are integrated directly into the workflow state, pausing execution until a human operator provides approval.

Project Details

Status
Active
Timeline
2024-2025
Focus Areas
5 domains

Key Components

The architecture is built around these core systems, each designed for specific responsibilities in the agentic workflow.

Control Plane

Tenant management, policy engine, and PII vault for multi-tenant enterprise deployments with zero-trust security.

Intelligence Plane

LangGraph-powered reasoning engine with tool executor, PII redaction/rehydration, and output validation.

Temporal Orchestration

Durable workflow engine with persistent state, history logging, and guaranteed execution semantics.

Memory Systems

Short-term scratchpad store for session context and long-term vector database for knowledge retrieval.

Human Review

Workflow pause/resume capabilities for human-in-the-loop approval of sensitive operations.

Audit & Logging

Comprehensive audit logs and analytics for compliance, debugging, and performance monitoring.

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