A comprehensive framework for building, deploying, and orchestrating AI agents at enterprise scale. Define the infrastructure layer that powers the next generation of intelligent systems.
Ten core components form the foundation of enterprise-grade AI agent infrastructure
The open standard defining how AI agents communicate with tools, data, and services.
Centralized registry for discovering, deploying, and managing AI agents across the organization.
Unified interface for accessing multiple AI models with enterprise-grade controls.
Development frameworks providing advanced memory, RAG, evals, and debugging for building agents.
Conversational platforms enabling business users to build, test, and deploy agents through natural language.
Policy-based access control for dynamic, context-aware authorization decisions.
Distributed networking layer enabling seamless communication and coordination between AI agents.
Monitoring, tracing, and feedback collection for continuous agent improvement.
Semantic knowledge graph providing organizational context and shared intelligence to all agents.
Chat UIs, agent inboxes, voice IO, and human-in-the-loop controls that enable effective collaboration between users and AI agents.
MCP is an open standard that defines how AI agents communicate with external tools, data sources, and services. MCP Servers bridge AI models to enterprise systems, while MCP Gateways provide centralized control, security, and intelligent routing across all connected servers.
MCP Servers provide the connectivity that transforms AI models into capable agents. MCP Gateways act as intelligent routers, providing unified control, protocol translation, and enterprise-grade security across your entire agentic architecture.
The Agent Catalog serves as a centralized registry for discovering, deploying, and managing AI agents within an organization. It provides a curated marketplace of pre-built agents alongside custom agent definitions.
The Agent Catalog is essential for organizations looking to scale their agent deployments while maintaining governance and control. It's your single source of truth for all agents.
LLM Gateways provide a unified interface for accessing multiple AI models from various providers. They act as a critical abstraction layer enabling organizations to leverage the best models while maintaining consistency and control.
LLM Gateways are essential for production AI deployments, providing the reliability, security, and observability required by enterprise applications. Scale with confidence.
Agent Frameworks like LangChain, Letta, CrewAI, and AutoGen provide the development tools and abstractions needed to build sophisticated AI agents. They offer advanced capabilities for memory management, retrieval-augmented generation, evaluation, and debugging.
Agent frameworks accelerate development by providing battle-tested patterns for memory, reasoning, and tool use. Choose the framework that fits your use case—from simple chains to complex multi-agent orchestration.
Conversational platforms that empower business users, analysts, and domain experts to build, test, and deploy AI agents simply by describing what they need. Just talk or type your requirements, have a conversation with the agent builder, and watch your agent come to life—no coding required.
Self-service platforms unlock the long tail of agent use cases by enabling domain experts to create agents themselves. Reduce the bottleneck on engineering teams while ensuring every agent meets organizational standards for security and quality.
The "World Model" for your AI agent workforce. Just as employees need onboarding and company updates to function effectively, agents need a centralized source of truth about the organization—its structure, priorities, and current events.
Without shared context, your Sales Agent doesn't know that Support is handling a massive outage for the same customer. The Context Fabric bridges this gap—enabling semantic disambiguation (knowing which "Mike" to email) and ensuring every agent operates with the full organizational intelligence.
Policy-Based Access Control (PBAC) for dynamic, context-aware authorization decisions. The Authorization Fabric ensures every agent action is evaluated, gated, and audited in real-time to maintain security and compliance.
The Authorization Fabric is the security backbone of agentic systems, ensuring that every action is verified, every access is justified, and every interaction is compliant. Build trust through continuous verification.
The Agent Mesh is a distributed networking layer that bundles and integrates the core architectural components together. It enables seamless communication, coordination, and collaboration between AI agents across organizational boundaries.
Comprehensive observability infrastructure for understanding agent behavior, performance, and outcomes. Feedback loops enable continuous improvement through user signals, automated evaluation, and runtime telemetry.
Observability transforms agent deployments from black boxes into transparent systems. Feedback loops close the gap between agent behavior and user expectations, enabling data-driven optimization and rapid iteration.
The touchpoints where humans interact with AI agents—from conversational chat interfaces to task management dashboards. Well-designed interfaces bridge the gap between agent capabilities and user needs.
Effective interfaces make agent capabilities accessible and trustworthy. From simple chat to sophisticated dashboards, the interface layer determines how naturally users can collaborate with AI agents to accomplish their goals.
These architectural components work in concert to enable enterprise-grade AI agent deployments
The Agentic Architecture provides a blueprint for organizations building intelligent systems at scale. MCP Servers & Gateways establish connectivity, the Agent Catalog organizes and governs agent assets, and LLM Gateways provide unified model access with enterprise controls.
Agent Frameworks accelerate development with sophisticated tooling for memory, RAG, and evaluation. Self-Service Agent Creation democratizes agent building through conversational interfaces. The Enterprise Context Fabric provides shared organizational intelligence, while the Authorization Fabric enforces security through policy-based access control. The Agent Mesh enables seamless multi-agent coordination across boundaries.
Observability & Feedback Loops provide visibility and continuous improvement, and Human-Agent Interfaces ensure that users can effectively collaborate with AI agents. Together, these components form a cohesive foundation for the next generation of intelligent enterprise systems.