The challenge
In a large microservices estate, a seemingly small change can ripple across many services. When an AI coding agent modifies one component, the expensive question is always the same: what else does this affect? Every change forces engineers to reconstruct the same relationships by hand.
The repeated questions: What changed? Who depends on it? Which services consume it? Which business processes are affected? What needs to be tested? What could break in production? Across tens or hundreds of services, rebuilding those answers for every change is slow and error-prone.
Our approach
The Architecture Intelligence Platform maintains an explicit architectural and dependency graph. When an AI agent modifies a component — say, a payment component — the system immediately identifies which services, components, processes and applications depend on it. Impact analysis happens before and after implementation, rather than rediscovering the architecture every time.
For a change to the Payment component, the maintained graph instantly surfaces the blast radius: Billing, Ledger, Notifications, Reporting and Checkout.
Why the usual approaches fall short
Grep, tribal memory and hope do not scale — the dependency map lives only in engineers' heads and is reconstructed from scratch on each change, right up until the one time it isn't.
| What change needs | Rediscover from source each time | Query a maintained graph |
|---|---|---|
| Know the blast radius | Reconstructed by hand, per change. | A direct query against the current graph. |
| Ship AI changes with confidence | Risk is estimated, not known. | Affected services and tests are enumerated. |
| Keep traceability | Relationships are undocumented. | Dependencies are an explicit, current artifact. |
Technology fit
A-Concept provides the structured behavioral components the graph can reason about; the Architecture Intelligence Platform maintains the relationships between components across repositories and services; AIS uses the impact information during development and validation of a change.
How it is implemented
Because features and their references are explicit, the platform can answer an impact query directly — no repository scan required.
entity('Payment') { feature('Authorize') { ref:authorize } feature('Release Funds') { ref:releaseFunds } } // asked before AI generates the change: impact(of: feature('Release Funds')) // → billing, ledger, notifications, reporting
Business outcome
Impact analysis becomes near-instant: less manual investigation before every change, faster AI-generated changes, better release confidence, reduced deployment risk and improved traceability across the estate.
AI can change code in seconds. Enterprise architecture needs to understand the consequences just as quickly.