WHAT CAUSES THIS?
Why it breaks in production
No stable contract around the legacy operation.
- Side effects are not idempotent.
- Read and write models disagree.
- UI automation is used where a controlled adapter could expose a safer boundary.
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Legacy applications often encode critical business rules in database procedures, batch jobs, UI workflows and undocumented side effects. AI integration fails when those constraints are hidden behind a thin tool wrapper.
DEMAND LANGUAGE / REAL-WORLD PROBLEM
“It works in the demo — but will it work in daily operations?”
“How do we measure whether the problem is actually solved?”
WHAT CAUSES THIS?
No stable contract around the legacy operation.
architecture_for AI LEGACY SYSTEM INTEGRATION
We identify stable business capabilities and wrap them with narrow, observable contracts. AI stays outside the source-of-truth boundary; adapters own validation, idempotency and state verification.
Separate read and write capabilities, constrain service identities and prevent model-generated input from bypassing domain validation.
Measure adapter latency, legacy saturation, queue depth and batch interaction. Backpressure and bounded concurrency protect systems that were not designed for agent-scale request patterns.
legacy systems · APIs · adapters · AI agents · integration
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CTO / CIO FAQ
Not always. A controlled integration boundary can create value and evidence before a larger modernization decision.
No. RPA can be one adapter mechanism, but production integration still needs contracts, state verification, identity and recovery semantics.
That is usually a high-risk shortcut because it bypasses application invariants and audit semantics. Prefer explicit domain operations.
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