The operational problem
Root-cause analysis slows down when production behavior depends on legacy code, hidden dependencies, and expert knowledge that is difficult to access during incidents.
What changes for the service
A code intelligence agent combines parsing, static analysis, code-aware retrieval, and LLM reasoning to explain modules, trace dependencies, and identify likely impact areas.
How it works in the service
Inside the service
Support and transition teams get a code-aware companion for the questions that stall an incident: which modules are involved, what calls what, and where a failure is likely to propagate.
Why it is delivered this way
Code ownership and operational ownership are often different teams. RED Reply uses this capability during managed transitions and modernization work so the operations side can reason about a system it did not build — with engineers, not the tool, making the call.
Accountable delivery
This capability is not sold as a product. RED Reply operates it as part of a managed service, with named service roles responsible for quality, escalation, and outcomes. Automated steps are scoped, logged, and reversible, and the actions that change a system or reach a customer stay under human control.
What it uses and produces
Inputs
- Source code
- Parsed functions
- Call graphs
- Runtime error context
- Repository metadata
Outputs
- Code explanations
- Dependency chains
- Impact analysis
- Root-cause hypotheses
- Onboarding summaries
Integrations
- Git repositories
- Graph database
- Vector database
- Incident tools
- Secure model runtime
How it is built
The pattern builds a private code knowledge layer with parsed symbols, call graphs, embeddings, and retrieval controls before exposing answers through a governed agent interface.
