Introduction
While preparing my paper “Addressing functionality gaps, data integrity, and system interoperability in enterprise systems,” I kept coming back to the same frustration: production incidents are still far too manual. We spot a timeout, race through dashboards, hack together a hotfix, and hope nothing else breaks. The paper proposes a different path - an AI-assisted error mitigation method that detects issues early, drafts a response, and pulls humans in only when judgment is required.
The emphasis is on mitigation: unblock the user, document the context, and let engineers decide what ships. Think of it as a co-pilot for incident response rather than a black-box healer.
I shared these ideas at the MANCOMP - Managed Complexity workshop during BIR 2025, walking through the prototype and gathering feedback from practitioners who live with the same architectural and operational constraints every day.
📸 Gallery
Why Tactical Gaps Pile Up
Enterprise systems rarely fail because of a single catastrophic bug. They erode under tactical gaps - missing functionality trapped behind architecture debt, data integrity problems caused by orphaned records, and brittle integrations that drift from external APIs. Research on ERP repair cycles and digital transformation backs that story: teams burn weeks on recurring fixes, data quality slips, and interoperability headaches stall business initiatives. The takeaway is simple - these problems demand a faster feedback loop than “file a ticket and wait for a sprint.”
How the AI-Assisted Mitigation Loop Works
The method I outlined centers on a “Healer” service that wraps your application with a safety net. When something goes sideways, the service collects context, builds a prompt, and asks an AI model for a potential patch that engineers can approve (or reject). Before anything touches production, the proposed code is unit-tested and queued for human approval. The runtime loop looks like this:
- The application surfaces an exception or timeout and hands the event to the Healer.
- A user approved a fix attempt.
- The Healer prepares a structured prompt with stack traces, payloads, and guardrails.
- An AI service drafts the mitigation - query tweaks, parameter sanitizers, schema adapters.
- Unit tests run automatically; failures halt the flow or a fix is applied live.
Three Scenarios That Drove the Design
To keep the scope clear, I validated the method against three common pain points:
- Functionality timeout: slow database lookups block a feature. The AI suggests an optimized query to get users unstuck.
- Data integrity violation: orphaned records crash a view rendering. The mitigation isolates the invalid rows so the rest of the records can be viewed, all while logging the incident for a proper data repair later.
- API attribute mismatch: a third-party service changes a payload. The mitigation layer intercepts the request, changes the attribute, and keeps the integration humming.
Each scenario keeps humans in the loop-the point is to give teams leverage, not to let an LLM edit code blindly.
Adding Proactive Detection
The base approach is reactive: listen for exceptions and move fast when they appear. The extended approach layers in log anomaly detection, so we can spot silent degradations-background jobs that slow down, integration retries that spike, or validation warnings that never bubble to the UI. When the anomaly service flags something, the Healer treats it like any other incident, triggering the AI workflow and presenting a mitigation before users start filing support tickets.
Human Oversight, Guardrails, and Scope
Runtime patching is only useful if it is trustworthy. That is why the prototype enforces automated tests, audit trails, and rollbacks. Every mitigation is stored as an ErrorEvent. Approval happens through an admin dashboard, and the system can roll back to the previous state if a change misbehaves. For anyone curious, the experimental code is open-source: github.com/matissg/healer.
This is AI-assisted error mitigation, not autonomous self-healing. The system does the heavy lifting of triage and draft fixes, but the final decision stays with a user. Today, I am comfortable deploying it in development and staging, or as a tightly gated safety net in production under close supervision. The controls are there, yet the method is still prototype-level and should be treated as such.
How It Differs from Traditional Tooling
Most observability stacks stop at alerting. Platforms like Sentry and New Relic are excellent at surfacing failures and performance regressions, but they still leave the fix - and the coordination overhead - to humans. Even with automation hooks, engineers shoulder the last-mile response. In contrast, the method in the paper pairs detection with an AI-assisted mitigation pipeline that executes in real time, is test-backed, and never skips human oversight. It is a middleware layer purpose-built to turn “we found a problem” into “here is the proposed remedy, ready for your approval.”
Final Thoughts
Short-term, tactical glitches will always creep into enterprise platforms. The goal is to make responding to them boring-in the best possible way. By blending AI-assisted mitigations with automated validation and human review, we can unblock users faster, keep data clean, and adapt integrations without derailing roadmaps. Outside this project, I am also experimenting with AI-assisted data fixes: surfacing historic data migration jobs to an AI layer so operators can chat through edge cases (like removing stubborn records) and have the system propose a safe, audited data correction. If you have a system that is currently taped together with shell scripts and optimism, I would love to hear how this approach could help.