Orchestrating AI Agents Across Diverse Enterprise Systems
This article discusses the emerging trend of enterprise AI agents and the architectural challenge of orchestrating them across various systems of record. It highlights OutSystems' approach to being a neutral 'Switzerland' layer, integrating and coordinating agents without owning the underlying data, addressing issues like shadow AI and token cost management. The core system design problem is building a flexible platform to manage and integrate numerous AI agents and their interactions with disparate enterprise data sources.
Read original on The New StackThe Rise of Enterprise AI Agents and Orchestration
The enterprise software landscape is rapidly evolving with the advent of AI agents, which are intelligent entities capable of performing tasks, making decisions, and interacting with various systems. Major vendors like SAP, Salesforce, and ServiceNow are developing their own agent platforms, typically deeply integrated within their respective ecosystems. This creates a challenge for enterprises that operate with a heterogeneous mix of systems of record, leading to potential vendor lock-in and fragmented AI capabilities.
OutSystems' 'Switzerland' Approach to Agent Orchestration
OutSystems positions itself as a neutral orchestration layer, or 'Switzerland', for enterprise AI agents. Instead of being a system of record, its platform focuses on integrating and coordinating agents across disparate enterprise systems. This approach allows organizations to leverage AI agents with data from various sources without being tied to a single vendor's ecosystem. Historically, OutSystems has played a similar role as 'glue' between commercial off-the-shelf solutions, making this a natural evolution of its platform.
Architectural Implication: Decoupling Agents from Data Sources
A key architectural principle highlighted is the benefit of decoupling AI agents and their orchestration layer from the underlying systems of record. This promotes flexibility, reduces vendor lock-in, and allows enterprises to build agentic workflows that span multiple data silos. The orchestration layer becomes responsible for secure data access, context management, and workflow routing.
Addressing Shadow AI and Token Cost Management
The proliferation of AI agents introduces new challenges for IT departments, including 'shadow AI' (unmanaged AI agent usage) and significant operational costs associated with token usage from large language models (LLMs). OutSystems addresses these by providing a central control plane for agent evaluation, guardrails, and semantic search. For token cost management, the platform offers model flexibility, allowing customers to bring their own models or route requests to the most cost-effective LLM via services like Amazon Bedrock, and potentially optimize token usage through an 'Enterprise Context Graph' that provides more concise input to models.
- Model Context Protocol (MCP) and Agent2Agent (A2A) services: These protocols are exposed by the OutSystems Agent Experience platform, enabling developers to build and extend applications with third-party coding tools and facilitating communication between agents.
- Open Systems Philosophy: The platform emphasizes open access and integration with various coding tools and AI models, recognizing that developers freely move between technologies. Closing environments leads to obsolescence.
- Orchestration Engine Evolution: The platform's prior role as an orchestration engine across many systems (e.g., for fund onboarding) provides a foundation for its current AI agent orchestration capabilities, demonstrating a scalable, extensible architecture.