Below is a comprehensive description of the Leena AI “Agentic AI Architecture” diagram, breaking down each component and illustrating how they interact to form a cohesive enterprise AI solution.
Touchpoints: Channels where users interact with the Agentic AI systems
Orchestrator: Central planning and coordination engine
Agents: Specialized AI agents based on roles or systems
Workflow Studio: To ground the Agentic system in Business Processes
Knowledge Management: Central repository of organizational knowledge
Permissions & Access Controls: Security layer ensuring compliance and role-based access
Observability and Governance: Oversight, governance, and continuous improvement
Security & Compliance: Adherence to industry standards like HIPAA, GDPR, SOC2, etc.
Positioned at the top of the diagram are multiple touchpoints through which users and systems can interact with the AI Agents:
These touchpoints represent the diverse channels used by employees or partners to engage with the Agents. The architecture is channel-agnostic, meaning any new channel can be integrated with minimal overhead.
At the core of the architecture is the Orchestrator, a centralized engine responsible for:
The Orchestrator ensures that requests are routed efficiently and that the right domain agent is engaged for each task. The orchestrator leverages multiple LLMs, including Leena AI’s proprietary WorkLM (powered by the Mixtral 8x22B and finetuned on 2 TB+ of proprietary enterprise data), Gemini models for multimodal capabilities, Claude models for higher complexity & reasoning prompts & OpenAI models for low latency requirements. The architecture is modular, ensuring newer models can be quickly plugged in for testing.
Directly under the Orchestrator, you see Agents: self-contained AI modules or services that handle specific tasks with agency. Key points about these Agents:
Agents can be powered by or integrated with Large Language Models (LLMs) such as GPT-3/o1, Anthropic Claude 3.7, Gemini, LLaMA, or other foundational models.
The choice of LLM can be dynamic based on the use case, data sensitivity, or performance requirements.
AOP is to agents what SOP is to humans. AOP grounds the agent in your company’s Business Processes, for eg, the PO creation agent has to follow your company’s specific PR creation, approval, and then PO creation process. AOPs allow Agents to “ingest” detailed process maps (text, flow charts) and follow the Business Process at run-time without having to create complex, resource-intensive workflows. These can simply be your existing SOPs or Business Process Flow Diagrams, or even just written text.
Each Agent is equipped with the domain expertise required to solve certain types of problems—whether HR, IT, Finance, or something else. Each Agent is an Orchestrator in itself and might be using different base LLMs as required by the role of that agent.
If an agent needs help from another agent, it will go back to the orchestrator and ask for that help. In the future, we plan to allow for agent-to-agent communication directly too.
Each agent is typically integrated with relevant third-party systems or databases (ServiceNow, Salesforce, BambooHR, Zendesk, Coupa, Snowflake, Confluence, and so on) to facilitate end-to-end process automation. The Agents interact with these applications to retrieve or update data, orchestrating multi-step processes across the enterprise.
Agents form the backbone of the system’s intelligence—modular, specialized, and easily updatable as new models or technologies become available.
Skills of an agent are Enterprise-specific deterministic Workflows, which are configured in the Workflow Studio. Each agent has access to multiple Workflows. Customers can configure templates available in the Workflow Studio according to their needs to quickly get up and running. This is extremely important because you need to ground the Agent into every company’s specific business processes.
The Workflow Builder comes out of the box with over 5000+ pre-built templates across 1000+ enterprise application integrations, including Workday, SAP, ServiceNow, Salesforce, Oracle, UKG, etc.
Know more about Workflow Studio here
The memory of an agent is external knowledge it has access to (or fine-tuned on), Company-specific structured/unstructured knowledge like Process guides, how-tos, etc, or even examples of how to do things. All of these are made available via the Knowledge Agent.
KM has out-of-the-box integrations with:
In the center, bridging the Domain-Specific Modules, there is a robust Permissions & Access Controls layer. This ensures:
Leena AI’s Responsible AI framework governs continuous service improvement and ensures ethical, compliant use of AI. It includes:
This layer helps organizations comply with internal governance policies and external regulations while maintaining user trust.
Leena AI is used by over 500+ enterprises globally and has various industry certifications and compliance with many regulations:
Other standards or certifications may be relevant depending on the specific industry or geographical location. The system is designed to be private and compliant, with data encryption, secure data handling, and audit trails.
Leena AI’s Agentic AI Architecture is designed to provide an end-to-end solution for enterprise-grade AI services. By combining a central Orchestrator, specialized Agents, the Workflow Studio, Knowledge Base, and out-of-the-box integrations, organizations can automate complex processes across different domains while maintaining strict security, compliance, and ethical standards. The incorporation of a Responsible AI framework ensures transparency, accountability, and continuous improvement—key factors for successful AI adoption in modern enterprises. Learn More here!
A: In the context of AI, the “agentic definition” refers to an AI system’s capacity for agency. This means the AI can perceive its environment, make independent decisions, and take autonomous actions to achieve specific goals. It’s about AI moving beyond passive responses to become an active participant in processes.
A: Essentially, Agentic AI is an advanced form of artificial intelligence that doesn’t just answer questions or provide information, but actively performs tasks and completes actions on behalf of users. The Agentic AI definition emphasizes its ability to combine knowledge with action to get things done across various enterprise systems.
A: While both are advanced AI, Agentic AI is primarily focused on action and task completion within enterprise systems, guided by specific workflows and goals. Generative AI, on the other hand, excels at creating new content (text, images, code). An Agentic AI system might use generative AI capabilities as one of its tools, but its core purpose is to act as an intelligent agent that executes tasks.
A: An agentic workflow is a defined sequence of steps, decisions, and actions that an Agentic AI system follows to complete a specific business process. These agentic AI workflows help businesses by automating complex, often multi-system tasks (like employee onboarding or IT support resolution), ensuring consistency, improving efficiency, and freeing up human employees for more strategic work.
A: Agentic AI frameworks refer to the comprehensive architecture and underlying structure that enables Agentic AI to operate effectively, reliably, and securely within an enterprise. This framework typically includes components like an orchestrator, specialized agents, a workflow studio, knowledge management systems, integration layers, and robust security and permission controls. They are important because they provide the necessary foundation for scalable, manageable, and “future-proof” Agentic AI solutions.
A: Implementing Agentic AI offers several key benefits, including: * Enhanced employee experience by providing a single, intelligent point of contact for tasks and information, reducing confusion from too many apps. * Increased productivity by automating routine tasks and handling complex cross-system use cases. * Improved operational efficiency by streamlining processes across various departments like HR, IT, and Finance. * Reliable and trusted assistance, as enterprise-grade Agentic AI is designed to avoid hallucinations and follow strict data permissions.
A: Agentic AI workflows improve the employee experience by making interactions with company systems seamless and intuitive. Instead of employees needing to know which system to use for a specific task (e.g., requesting leave, resolving an IT issue), the Agentic AI, guided by its workflows, handles these processes behind the scenes. This means employees get their tasks done quickly and easily, reducing frustration and allowing them to focus on their core responsibilities.