Assessing Component Autonomy: Strengthen Your Software’s Adaptability

Evaluate and enhance the autonomy of your software components.

In my previous article, “Build Adaptable and Resilient Software Systems by Unlocking Component Autonomy”, I emphasized autonomy as a critical property of well-designed software components:

When a system’s components become too interdependent, even a small change can have wide-ranging effects. For example, if a modification in one component triggers changes in several others, it signals architectural entanglement. This issue is especially problematic when different teams are responsible for different components, because any change requires extensive communication and coordination across teams, disrupting priorities and backlogs. Such complexity not only increases technical overhead but also adds organizational and communication burdens, ultimately resulting in longer lead times and a higher likelihood of errors.

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In software architecture, autonomy means that a system’s component can operate and evolve independently, reducing the need for frequent (inter-team) coordination.

Autonomy is not binary; it exists on a spectrum from total dependency to complete independence. Instead of viewing autonomy as something you either have or do not have, individual entities should always strive to become more autonomous. Therefore, if your team is working on a component that ranks low on the scale, you should seek ways to increase its autonomy rather than accepting its dependency.

In the article, I also outlined several distinct aspects of component autonomy, summarized here:

• Functional Autonomy: Each component should have a clearly defined and distinct responsibility, ensuring its functionality is clearly separated from other system parts. This minimizes coordinated changes across components.

• Data Autonomy: Components should independently manage their internal data without relying on shared mutable data or schemas, preventing data-driven changes from cascading across components.

• Runtime Autonomy: Components should operate independently at runtime, remaining functional even when related services become temporarily unavailable. They should include mechanisms enabling continued operation without immediate external input.

• Activation Autonomy: Component with high activation autonomy decide independently when to perform actions instead of depending on external triggers or orchestrators. This self-driven behavior enhances responsiveness and reduces external dependency.

• Scaling Autonomy: Autonomous components can handle increased loads independently without external bottlenecks limiting their performance. Their scalability primarily depends on their own design and allocated resources.

• Deployment Autonomy: Teams should be able to update or redeploy their components independently, without requiring synchronized changes in other system areas. This autonomy supports frequent releases and minimizes system-wide disruptions.

• Solution Autonomy: Solution autonomy allows teams responsible for a component to independently choose technologies, tools, and strategies. This freedom fosters innovation and rapid adaptation to changing business needs.

After discussions with some of you who’ve sent me feedback, I have added another aspect - you’ll find it in the original article but I also will summarize it here:

Infrastructure Autonomy

Component teams with low infrastructure autonomy rely on manual work by other teams to provision infrastructure they need. Conversely, teams with high infrastructure autonomy can independently provision and manage their infrastructure, often supported by platform or infrastructure teams providing self-service capabilities. Teams with high infrastructure autonomy rarely trigger additional manual tasks in other teams, as they utilize provided self-service infrastructure tools:

As part of my regular architecture reviews with clients, I pay special attention to component autonomy. To facilitate this, I've developed heuristics to assess component autonomy systematically. In this article, I share these heuristics, structured as key questions designed to help you evaluate autonomy across all aspects. Throughout the listing, the component being evaluated will be referred to as "Component A".

Functional Autonomy

When you make changes to Component A, do you typically need to adjust other components to maintain the overall correctness of the system? If so, how many components are affected?
A high number of affected components indicates low functional autonomy. If you cannot modify Component A without simultaneously adjusting and redeploying other components, it suggests that Component A's functional boundaries are not well-defined. This issue might arise from unclear or overlapping responsibilities or from components bypassing clearly defined interfaces and directly depending on Component A's internal implementation.

Do changes in Component A often cascade into larger system modifications, suggesting a lack of clear boundaries in functionality?
If changing Component A frequently requires adjustments to multiple other components, this signals unclear functional boundaries and a higher level of architectural coupling. Ideally, modifications should have a limited impact radius, affecting as few additional components as possible.

Data Autonomy

When modifying the data stored by Component A or its associated schema, do you also need to adapt or update other components?
If the answer is yes, it suggests that Component A relies on shared mutable data, significantly reducing its autonomy. Dependence on shared mutable data typically requires synchronized changes and coordinated deployments across multiple components to prevent system errors. Ideally, components should manage their own data independently, minimizing cross-component dependencies.

Runtime Autonomy

When a related service or dependency becomes (temporarily) unavailable, can Component A continue operating effectively?
To assess this, first identify all components that Component A communicates with, including data providers, synchronous and asynchronous services, orchestrators, and data sinks. Next, evaluate how Component A behaves when each of these components is unavailable. Can it still function normally and continue processing requests without immediate external input?

Discuss these scenarios with your team or leverage Chaos Engineering (TODO link https://en.wikipedia.org/wiki/Chaos_engineering) techniques to simulate and test component resilience under failure conditions.

Activation Autonomy

Does Component A independently decide when to act, or does it rely on external triggers?
Assess the conditions under which Component A initiates tasks. If other components control or heavily influence when Component A executes its tasks, this indicates lower activation autonomy.

When changes occur in Component A, how many other components require modifications?
Frequent simultaneous updates to other components alongside Component A suggest lower activation autonomy. This happens because other components have excessive knowledge of, or dependency on, Component A's execution logic, increasing the system’s coupling.

Do many of your team's tasks depend on changes in components beyond Component A?
If the internal logic or interfaces of other components frequently trigger changes within Component A, it demonstrates a lack of isolation in activation logic, further reducing activation autonomy.

Scaling Autonomy

Are there other components (or infrastructure) that have the same scaling behavior as Component A?
If other components share the same scaling pattern as Component A, it indicates they must handle similar throughput levels. This dependency can become problematic if any of these components cannot meet the required throughput, potentially creating bottlenecks and limiting Component A’s ability to scale independently.

Does Component A perform non-business-related actions (like committing transactions or cleaning up data) solely to maintain consistency?
These actions suggest there may be cross-component consistency constraints, especially in distributed systems. Such constraints typically introduce additional overhead, requiring components to perform extra work that can impede their independent scalability.

Deployment Autonomy

When deploying Component A, do you have to deploy other components at the same time? Does deploying Component A require stopping, restarting, or redeploying other components?
Consider the extent to which deploying Component A affects other parts of the system. If deployment of Component A frequently involves modifying or coordinating with other components—or requires collaboration with other teams—this indicates lower deployment autonomy.

Does your team have the authority and flexibility to update and release Component A independently, without obtaining approval or coordination from other teams or central entities?
Frequent and efficient deployments depend heavily on a team's autonomy. Cross-team communication and coordination can significantly slow down deployments and introduce unnecessary overhead.

Infrastructure Autonomy

Does the need for infrastructure changes by Component A induce manual work in other teams?
If infrastructure changes for Component A routinely depend on manual tasks from other teams (such as platform or infrastructure teams), this indicates low infrastructure autonomy. For example, if Component A requires new data storage, low autonomy would mean another team needs to provision it manually. In contrast, high infrastructure autonomy would allow your team to directly self-serve and quickly integrate pre-configured infrastructure resources, minimizing dependency and delays.

Solution Autonomy

Does the team responsible for Component A have the freedom to adopt technological solutions or make decisions about Component A’s design and evolution without needing extensive coordination with other teams?
Two key aspects influence this: First, Component A must be architecturally isolated enough to enable technological independence, allowing the team to freely select programming languages, databases, or frameworks. Second, the organizational culture should empower teams by granting them sufficient freedom to explore, choose, and implement their own solutions, rather than strictly regulating the technologies and processes they use.

Summary

Below, you’ll find all aspects of autonomy and their associated review questions: