By Christian Siegers, Principal, KPMG Advisory
For decades, architecture testing has been the quiet backbone of responsible software engineering. While application testing ensures features work, architecture testing ensures that systems behave. It verifies how components collaborate, how constraints propagate, and how non‑functional requirements—performance, robustness, recoverability—will hold under stress or change. In the era of distributed systems, microservices, and cloud-native patterns, this discipline has always been essential.
Yet despite its importance, I increasingly see that architecture testing is still undervalued in many organizations. Too often, teams assume that if the application behaves correctly during functional testing, the architecture must be sound. But architecture often fails long before code does. And now, with the rapid emergence of agentic AI, the consequences of not testing architecture thoroughly have become far more visible—and far more severe.
The Changing Nature of Software Systems
Historically, architecture testing functioned as a safeguard against emergent complexity in distributed systems. Whenever an organization deployed a network of interdependent services, message buses, caches, and APIs, the potential for unforeseen interactions grew. Even before AI entered the picture, architects confronted the reality that large systems behave in ways no single engineer fully anticipates.
This is precisely where architecture testing proved its value: it gave teams structured ways to explore risk early, validate critical assumptions, and catch architectural flaws before they hardened into production failures. This need remains, but agentic AI transforms the challenge.
When Software Begins to Act
Agentic AI systems do something unprecedented: they behave over time. A traditional LLM is a one‑shot prediction engine—input in, output out. An agent, by contrast, is a stateful, goal‑driven actor. It plans, executes, observes, adapts, and repeats. Agentic AI is increasingly understood as AI that can plan, remember, reflect, and actively work toward goals—something neither classical AI nor modern generative models were designed to do.
This autonomy arises from the way these systems are architected: a reasoning engine supported by memory, tool‑use capabilities, and a control loop coordinating their interaction. Some architectures draw on symbolic planning traditions, while others rely on neural‑generative methods. Increasingly, the most resilient designs combine both, forming hybrid neuro‑symbolic architectures.
For architects, this means the software is no longer simply executing instructions—it is making decisions within the constraints of your architecture. And that changes everything.
Why Traditional Testing Breaks Down
Agentic systems challenge traditional testing practices in several fundamental ways.
First, these systems are inherently non‑deterministic. A test that succeeds at 9:00 might fail just minutes later simply because the agent followed a different reasoning path. This creates a widening ‘verification gap,’ where deterministic enterprise systems and probabilistic, adaptive agents operate according to fundamentally different reliability expectations.
Second, these agents operate within environments that are constantly shifting—APIs, user interfaces, databases, and document stores all evolve independently of the agent itself. Because agents are expected to detect these changes and adapt their behavior, long‑held architectural assumptions about stability and interface contracts become far more fragile. As agents adjust to UI variations, schema modifications, or altered workflows, it becomes clear that entirely new testing methods are needed to ensure systems remain robust
Third, agentic AI introduces a new level of emergent behavior. Operating through multi‑step reasoning loops and tool interactions, agents can develop strategies or intermediate actions that were never explicitly designed or anticipated. While emergence has always existed in complex distributed systems, with agents it becomes the rule rather than the exception.
All of this places new pressure on architecture itself. Architecture is no longer only a structure of components; it is the operating environment for autonomous decision‑makers.
A Personal Reflection: The Growing Gap I See
In my day‑to‑day work, I’m seeing a widening gap between the complexity of our systems and the rigor of the architectural validation we apply to them. For many teams, architecture testing is still viewed as a periodic exercise or a nice‑to‑have, overshadowed by functional testing and automated pipelines. But agentic AI exposes architectural weaknesses far more quickly and forcefully than traditional systems ever did.
When an autonomous agent misinterprets context, takes an unexpected path, or acts on outdated interfaces, the root cause is almost always architectural—not algorithmic. This is why I believe architecture testing can no longer remain an optional discipline. It must become continuous, intentional, and deeply integrated into how we build intelligent systems.
In other words: agentic AI hasn’t created the architectural gaps—it has made them impossible to ignore.
The New Imperative: Architecture Testing for Agentic Systems
If architecture testing was once about validating performance, scalability, and integration, it is now also about validating behavioral boundaries—how an agent interprets instructions, how it uses tools, how it navigates uncertainty, and how it recovers from mistakes.
As enterprises embed agents more deeply across their ecosystems—from test automation to operations and customer-facing workflows—the architectural stakes increase significantly. Many organizations are already moving in this direction; in fact, one in four has incorporated agentic AI into its QA pipelines, shifting testing away from static execution and toward continuous, adaptive intelligence.
This means architects can no longer rely on downstream testing alone. The architecture itself must be tested as the foundation upon which autonomous agents operate. Several dimensions become especially critical:
1. Testing the Decision Loops, Not Just the Outputs
Agentic systems are defined by trajectories—sequences of decisions, tool calls, and corrections. Architecture testing must therefore assess not just whether the final output is acceptable, but whether the path the agent followed is correct, safe, and aligned with system goals.
2. Validating the Stability of the Agent’s World
The architecture defines the interfaces, data contracts, and environmental cues the agent relies upon. Because agents adapt to interface changes, architectural testing must ensure those changes remain within safe and predictable boundaries. Studies on agentic testing emphasize agents’ continuous perception of changes in UI and API structure
3. Governing Hybrid Reasoning Architectures
As agentic systems evolve toward hybrid neuro‑symbolic forms, architects must test how symbolic planning and neural inference interact—and where they can fail. Research points out governance gaps in symbolic systems and the need for more robust architectural frameworks.
4. Emergence and Safety Under Change
Architecture tests must increasingly resemble simulation environments, probing how agents behave under uncertainty, incomplete information, or unexpected mutations in the environment.
In short: architecture testing becomes the mechanism through which we model the boundaries of acceptable autonomy.
A Discipline Transformed
The shift to agentic systems does not diminish the importance of classical architecture testing—it magnifies it. Architects must now build and validate systems where intelligence is not static but active, adaptive, and self‑directed. Every architectural decision becomes amplified through the behavior of agents operating within it.
What I am observing is that architecture testing—once a highly valuable but sometimes overlooked practice, now determines whether organizations can safely and effectively adopt agentic AI. It is no longer enough to design architectures; we must continuously test how autonomous entities understand, traverse, and reinterpret them.
As AI agents move from isolated experiments to mission‑critical components, architecture testing becomes a strategic safeguard. It ensures that autonomous systems remain predictable without being constrained, adaptable without being ungoverned, and powerful without becoming unpredictable.
Ultimately, the future reliability of intelligent systems will depend on how seriously we take architecture testing today.
For decades, architecture testing has been the quiet backbone of responsible software engineering. While application testing ensures features work, architecture testing ensures that systems behave. It verifies how components collaborate, how constraints propagate, and how non‑functional requirements—performance, robustness, recoverability—will hold under stress or change. In the era of distributed systems, microservices, and cloud-native patterns, this discipline has always been essential.
Yet despite its importance, I increasingly see that architecture testing is still undervalued in many organizations. Too often, teams assume that if the application behaves correctly during functional testing, the architecture must be sound. But architecture often fails long before code does. And now, with the rapid emergence of agentic AI, the consequences of not testing architecture thoroughly have become far more visible—and far more severe.
The Changing Nature of Software Systems
Historically, architecture testing functioned as a safeguard against emergent complexity in distributed systems. Whenever an organization deployed a network of interdependent services, message buses, caches, and APIs, the potential for unforeseen interactions grew. Even before AI entered the picture, architects confronted the reality that large systems behave in ways no single engineer fully anticipates.
This is precisely where architecture testing proved its value: it gave teams structured ways to explore risk early, validate critical assumptions, and catch architectural flaws before they hardened into production failures. This need remains, but agentic AI transforms the challenge.
When Software Begins to Act
Agentic AI systems do something unprecedented: they behave over time. A traditional LLM is a one‑shot prediction engine—input in, output out. An agent, by contrast, is a stateful, goal‑driven actor. It plans, executes, observes, adapts, and repeats. Agentic AI is increasingly understood as AI that can plan, remember, reflect, and actively work toward goals—something neither classical AI nor modern generative models were designed to do.
This autonomy arises from the way these systems are architected: a reasoning engine supported by memory, tool‑use capabilities, and a control loop coordinating their interaction. Some architectures draw on symbolic planning traditions, while others rely on neural‑generative methods. Increasingly, the most resilient designs combine both, forming hybrid neuro‑symbolic architectures.
For architects, this means the software is no longer simply executing instructions—it is making decisions within the constraints of your architecture. And that changes everything.
Why Traditional Testing Breaks Down
Agentic systems challenge traditional testing practices in several fundamental ways.
First, these systems are inherently non‑deterministic. A test that succeeds at 9:00 might fail just minutes later simply because the agent followed a different reasoning path. This creates a widening ‘verification gap,’ where deterministic enterprise systems and probabilistic, adaptive agents operate according to fundamentally different reliability expectations.
Second, these agents operate within environments that are constantly shifting—APIs, user interfaces, databases, and document stores all evolve independently of the agent itself. Because agents are expected to detect these changes and adapt their behavior, long‑held architectural assumptions about stability and interface contracts become far more fragile. As agents adjust to UI variations, schema modifications, or altered workflows, it becomes clear that entirely new testing methods are needed to ensure systems remain robust
Third, agentic AI introduces a new level of emergent behavior. Operating through multi‑step reasoning loops and tool interactions, agents can develop strategies or intermediate actions that were never explicitly designed or anticipated. While emergence has always existed in complex distributed systems, with agents it becomes the rule rather than the exception.
All of this places new pressure on architecture itself. Architecture is no longer only a structure of components; it is the operating environment for autonomous decision‑makers.
A Personal Reflection: The Growing Gap I See
In my day‑to‑day work, I’m seeing a widening gap between the complexity of our systems and the rigor of the architectural validation we apply to them. For many teams, architecture testing is still viewed as a periodic exercise or a nice‑to‑have, overshadowed by functional testing and automated pipelines. But agentic AI exposes architectural weaknesses far more quickly and forcefully than traditional systems ever did.
When an autonomous agent misinterprets context, takes an unexpected path, or acts on outdated interfaces, the root cause is almost always architectural—not algorithmic. This is why I believe architecture testing can no longer remain an optional discipline. It must become continuous, intentional, and deeply integrated into how we build intelligent systems.
In other words: agentic AI hasn’t created the architectural gaps—it has made them impossible to ignore.
The New Imperative: Architecture Testing for Agentic Systems
If architecture testing was once about validating performance, scalability, and integration, it is now also about validating behavioral boundaries—how an agent interprets instructions, how it uses tools, how it navigates uncertainty, and how it recovers from mistakes.
As enterprises embed agents more deeply across their ecosystems—from test automation to operations and customer-facing workflows—the architectural stakes increase significantly. Many organizations are already moving in this direction; in fact, one in four has incorporated agentic AI into its QA pipelines, shifting testing away from static execution and toward continuous, adaptive intelligence.
This means architects can no longer rely on downstream testing alone. The architecture itself must be tested as the foundation upon which autonomous agents operate. Several dimensions become especially critical:
1. Testing the Decision Loops, Not Just the Outputs
Agentic systems are defined by trajectories—sequences of decisions, tool calls, and corrections. Architecture testing must therefore assess not just whether the final output is acceptable, but whether the path the agent followed is correct, safe, and aligned with system goals.
2. Validating the Stability of the Agent’s World
The architecture defines the interfaces, data contracts, and environmental cues the agent relies upon. Because agents adapt to interface changes, architectural testing must ensure those changes remain within safe and predictable boundaries. Studies on agentic testing emphasize agents’ continuous perception of changes in UI and API structure
3. Governing Hybrid Reasoning Architectures
As agentic systems evolve toward hybrid neuro‑symbolic forms, architects must test how symbolic planning and neural inference interact—and where they can fail. Research points out governance gaps in symbolic systems and the need for more robust architectural frameworks.
4. Evaluating Emergence and Safety Under Change
Architecture tests must increasingly resemble simulation environments, probing how agents behave under uncertainty, incomplete information, or unexpected mutations in the environment.
In short: architecture testing becomes the mechanism through which we model the boundaries of acceptable autonomy.
A Discipline Transformed
The shift to agentic systems does not diminish the importance of classical architecture testing—it magnifies it. Architects must now build and validate systems where intelligence is not static but active, adaptive, and self‑directed. Every architectural decision becomes amplified through the behavior of agents operating within it.
What I am observing is that architecture testing—once a highly valuable but sometimes overlooked practice, now determines whether organizations can safely and effectively adopt agentic AI. It is no longer enough to design architectures; we must continuously test how autonomous entities understand, traverse, and reinterpret them.
As AI agents move from isolated experiments to mission‑critical components, architecture testing becomes a strategic safeguard. It ensures that autonomous systems remain predictable without being constrained, adaptable without being ungoverned, and powerful without becoming unpredictable.
Ultimately, the future reliability of intelligent systems will depend on how seriously we take architecture testing today.