Originally posted on Medium.
Enabling Real-time, Enterprise-grade, Contextual Automation
Generative AI and Agentic AI have raised expectations for natural language system interactions and autonomous agents. However, Language Models have limited reasoning and planning capabilities and make a poor “intelligence layer”. Likewise, Agents have limited workflow execution capabilities and are not appropriate for mission critical processes. Existing solutions are marked by flimsy scaffolding, over-stated capabilities and naive use of memory, reflection, recursion, and behavior.
The Enterprise needs balanced solutions that leverage GenAI and Agentic AI capabilities, optimizing outcomes while mitigating risks and costs. There is a growing understanding that graphs are part of hybrid AI solutions that bridge deep learning and symbolic AI (i.e., Neuro-Symbolic AI) for optimized, safe, and explainable decisions and actions. This means the scope of graphs is expanding to include complex, real-world operational use cases. However, in many ways conventional Semantic Web and Labeled Property Graphs are sub-optimal for the demands of real-time, enterprise-grade contextual automation (tedious, manual design of static models with binary relationships and no change management, governance or temporal history, etc.).
EnterpriseWeb offers a Knowledge Graph-driven no-code platform that enables the declarative modeling of LangGraph agents in minutes, and provides a secure MCP based AI-Gateway for Context-as-a-Service, Middleware Tools-as-a-Service and Governance-as-a-Service.
Context-as-a-Service
Instead of being guided by Language Models, agents leverage EnterpriseWeb’s Knowledge Graph as a deterministic source of domain knowledge and real-time operational state/telemetry to contextualize decisions and optimize actions. In addition, the platform serves as a “Blackboard” providing shared-memory for agent coordination to enable complex, multi-step and long-running workflows, as well as temporal history for troubleshooting, audit and learning.
The platform knowledge-graph also radically accelerates, simplifies and improves the use of Generative AI for accurate, hallucination-free natural language interfaces and inferences. The platform provides an object abstraction that supports multi-model GenAI to eliminate tight-coupling and allow organizations to evolve with the rapidly changing models. In EnterpriseWeb, Language Models and other AI algorithms are typed objects with common access control, change management and governance.
Beyond GraphRAG, the platform’s Knowledge Graph acts as a control language over one-to-many models on-premises or in the Cloud. Instead of simply serving chunked documents as blunt context, EnterpriseWeb dynamically orchestrates AI using the Knowledge Graph to precisely ground multi-turn interactions and deterministically validate inferences so they can be used safely as managed inputs into controlled processes. By shifting most of the reasoning back to the Knowledge Graph the platform provides a uniquely low-token, low-latency, energy efficient GenAI solution.
In the AI era, an enterprise knowledge graph is the most valuable business asset. EnterpriseWeb’s no-code platform provides primitives, building blocks and services to ease and speed the design, use and maintenance of domain ontologies, while wrapping them in access control, change management, temporal history and strong governance. EnterpriseWeb enables a real-time digital representation of the business for a single source of truth that serves context, optimizes actions and keeps operations synchronized.
Middleware-as-a-Service
The platform takes tool-calling to the next level by exposing a library of middleware capabilities (communication, integration, transformation, automation, configuration, orchestration, etc.), implemented as serverless functions. Agents can walk the graph to discover tools. They can compose and configure tasks into complex workflows and orchestrate distributed solution elements.
In addition, the AI Gateway exposes an extensible catalog of typed objects representing applications, systems, services, databases, devices, cloud hosts and networks, and language models (“things not strings”) that can be discovered in the graph and included in automations and orchestrations. The catalog extends loose-coupling, enables declarative composition and supports highly dynamic operational solutions.
EnterpriseWeb can also configure LangGraph agents with hooks back to its suite of functional modules to weave GenAI NLP and Agentic AI in a unified platform experience with a rich design environment and management console.
Governance-as-a-Service
The AI Gateway enforces security and identity dynamically filtering what models and agents can access and do based on permissions. All agent interactions are managed as asynchronous transactions with retries, circuit-breakers, exception-handling, detailed logs and distributed trace.
Below are links to related blogs, demos and interviews.
For more information, email us at info@enterpriseweb.com to schedule a demonstration or arrange a trial.
Webinar/Demo: Context-as-a-Service
Blog Post: How to Tame a Stochastic Dragon
Webinar/Demo: Telecom Ontology
Blog Post: EnterpriseWeb vs Semantic Web
Interview: Agentic AI: How telecoms operators can advance towards autonomous operations
Blog Post: Lowering the cost of context for real-time, data-driven systems
Blog Post: GraphOps for the AI-enabled telco
Webinar/Demo: Graph-driven Orchestration
