What Is a Multi-Agent Architecture?
Modern AI rarely works alone. A multi-agent architecture is a system in which multiple specialised AI agents collaborate — each with its own role, tools, and decision-making capability — to complete complex, end-to-end tasks. Think of it as an AI workforce: an orchestrating agent breaks down a goal, delegates subtasks to specialist agents, and synthesises their responses into a coherent outcome. These architectures unlock capabilities that no single model could achieve alone, making them the backbone of enterprise-grade agentic AI.
Pega’s Point of View: Control Is Everything
The promise of multi-agent systems is exciting. The risk, if left unmanaged, is real. Autonomous agents operating without guardrails can produce unpredictable outputs, violate business rules, or expose sensitive data. Pega’s position is clear: to be predictable, reliable, and trustworthy, agents must operate within the context of a process, and be governed by the rules that define it.
In Pega, agents execute alongside and within structured workflows. They understand the case context they belong to, the business rules that must be respected, and the boundaries within which they are permitted to act. When you connect an external agent — say, a specialist model hosted elsewhere — by wrapping it in a Pega agent and calling it over A2A, you’re not just making a connection. You’re adding a layer of governance. Pega controls what goes in, how the response is interpreted, and whether it surfaces at all. The external agent gets to do what it does best; Pega makes sure it does so safely and in context.
Of course, you could skip that and call the external agent directly over (for example) REST. But that’s a very different proposition.
Two Approaches: REST vs A2A
REST API gives you raw, unrestricted access. You control every header, every message format, every prompt — manually. Input and output mapping is entirely your responsibility. The response comes back as-is, with no interpretation, no enrichment, no contextual framing. There are no constraints on which external models or hosting providers you use. Maximum flexibility; maximum responsibility.
A2A, by contrast, is configured through Pega’s rule forms, bringing structure and governance into the integration layer. You provide prompts and context to the Pega agent, which in turn prompts the external agent — meaning that the exact wording sent downstream is built dynamically by the Pega agent rather than being pre-configured at design time. The external agent’s response is in turn interpreted by the Pega agent, which decides how (and whether) to incorporate it into its own reply.
| REST API | A2A | |
|---|---|---|
| Configuration | Manual | Pega rule forms |
| Prompt control | Full, manual | Delegated to Pega agent |
| Input handling | Developer-managed, implicit in JSON construction | Input context explicitly provided through Agent rule |
| Response handling | Raw, uninterpreted | Interpreted and contextualised |
The Bottom Line
REST gives you a direct line. A2A gives you a smarter one. By routing external agent interactions through Pega’s A2A framework, you gain something REST alone can’t provide: a layer of process-aware intelligence that enriches prompts, interprets responses, enforces business rules, and keeps your agents firmly within the guardrails of your enterprise architecture. In a world where agentic AI is moving fast, that control isn’t a constraint — it’s a competitive advantage.