Every era of enterprise technology has added a defining layer to the stack. Mainframes gave us the system of record. Client/server gave us applications. The web gave us services and APIs. Cloud gave us elastic infrastructure. Now agentic AI is adding the next layer. Where the application layer executed instructions, the agent layer intercepts business signals, reasons, decides, acts, and self-optimizes. AI agents will become the enterprise’s operational tier, driving outcomes that once required humans stitching together a dozen applications, and will be awash in them by the hundreds, then thousands.

Agentic AI Breaks Your Architectural Assumptions

Enterprises can’t run these agents at scale because today’s architecture stacks were never designed to run any of them at all. The architectures you’ve spent decades hardening were built for request/response integration, predefined process flows, and human-paced workflows. Or in some cases, for the nightly batch cycle, where data arrives up to 24 hours after the fact. They won’t accommodate agents multiplying across every platform, pursuing goals, and self-optimizing. Enterprise architects must confront four realities that break old architectural assumptions:

  • Agent autonomy operates on a spectrum. Some agents execute tightly scripted, deterministic workflows; others reason, plan, and decide for themselves how to accomplish a goal. Enterprises will dial autonomy up or down by use case, risk, and regulation. Architectures built for a single execution pattern can’t govern, audit, and optimize software that ranges from fully predictable to fully autonomous.
  • Agents arrive from everywhere. No single vendor will build all of your agents. They’ll come from hyperscaler platforms, SaaS, APO and data platform vendors, low-code tools, and bespoke frameworks. Fragmented runtimes produce unpredictable behavior and compounding complexity as agent counts grow.
  • Agents need what legacy systems won’t give them. Your core business data, processes, and logic live in systems that lack the clean interfaces agents require. Direct connections create fragile integrations and data silos. It’s the design mistake that doomed early SOA implementations, now at agentic speed and scale.
  • Governance buried in agent code doesn’t scale. When guardrails live inside individual agents or a single vendor’s platform, every policy change becomes a refactoring project. Autonomous software demands governance that is centralized and enforced at runtime.

A New Layer Demands A New Runtime

This is why we built Forrester’s Agentic Runtime Architecture (ARA). It is deliberately a runtime, not another AI development platform or broad guide to architecting the entire agentic lifecycle (for that see our AI Platforms Wave and Architect’s Guide to Agentic AI). The ARA is the production-grade, event-driven execution environment where heterogeneous agents, regardless of origin, operate reliably, economically, and under enterprise control. It comprises nine core components: an event handler, context manager, toolbox, model repository, agent factory, agent orchestrator, optimization manager, agent governance guardian, and business observability. The Agentic Runtime Architecture is different because:

  • Business signals never wait in a batch style. The ARA analyzes incoming data, API triggers, and natural language and routes each event to the right agent with low latency, without rigid custom integrations.
  • Every agent must reason from the same facts and context. It grounds every agent in shared business ontology and working memory, exposes legacy and external capabilities as discoverable, governed tools, and matches the right model to each task.
  • Any agent from any platform runs as one workforce. It abstracts away each agent’s design-time origin and provides a scalable, fault-tolerant environment where agents make tool calls and collaborate via protocols such as A2A and MCP.
  • No action executes ungoverned. It intercepts every tool call, inference, and output and checks each against security, compliance, risk, and cost policies before execution, then ties agent activity to enterprise KPIs and turns those metrics into continuous improvement.

Choose To Use The ARA

The enterprises that treated APIs as a first-class architectural concern won the last decade; those that choose a runtime architecture for their agents will win this one. In the full report available to Forrester clients “Introducing Forrester’s Agentic Runtime Architecture,” you will find:

  • The seven design principles of agentic architecture. Lessons from event-driven applications and service-oriented architectures, translated into the design goals your agent layer must meet.
  • A reference architecture built from nine core components. Each component explained in depth, including what it does, why the ARA requires it, and example vendors that provide it today.
  • Two step-by-step “life of the agents” scenarios. An auto insurance claim and a time-critical perishable product recall, traced from raw business signal to governed outcome so you can apply the same lens to your own workflows.
  • Five imperatives for preparing for thousands of agents. Guidance on design-time builders, tool curation, independent governance, business metrics that matter, and the culture of rapid refactoring.

Read the report, then schedule a guidance session or inquiry.

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