Published work
Project Background
Troubleshooting platform failures required operators to investigate several sources of information before they could understand what was happening. A reported symptom rarely pointed directly to its root cause: signals had to be correlated with platform context, diagnostic outputs, and operational knowledge.
The main bottleneck was not the absence of information, but the time required to locate, connect, and interpret it. I identified an opportunity to turn this fragmented investigation process into a structured agentic workflow.
Solution Development
I designed and deployed a troubleshooting agent that connects operational signals, platform knowledge, and diagnostic tools. Instead of relying on a single prompt or static knowledge base, the system follows an iterative diagnostic loop in which it gathers context, selects the appropriate tools, evaluates their outputs, and determines the next useful step.
The agent was designed to support operators with evidence-based guidance. Its role is to reduce the search space and organize the investigation into a credible resolution path, while keeping operational decisions visible and understandable.
Diagnostic Workflow
The agent follows a structured process:
Receive a failure signal or a symptom reported by an operator.
Gather the platform context relevant to the incident.
Select and invoke diagnostic tools through controlled interfaces.
Correlate the returned signals with available operational knowledge.
Identify likely failure conditions and remaining uncertainties.
This loop allows the system to adapt its investigation as new evidence becomes available instead of producing a generic response from the initial description alone.
Operational Reliability and Evaluation
Building the agent required more than connecting a language model to internal tools. The workflow needed clear tool boundaries, predictable failure handling, and outputs that operators could evaluate before acting.
Operational validation focused on whether the agent selected useful diagnostic actions, interpreted tool responses consistently, and produced recommendations that moved the investigation forward. The system was evaluated according to its effect on the complete troubleshooting workflow, rather than only the quality of an isolated model response.
Impact
The deployed workflow improved issue-resolution speed by 50%. The agent reduced the manual effort required to move from an initial symptom to a well-supported diagnostic path, allowing operators to spend more time resolving incidents and less time searching across disconnected sources.
The project also demonstrated how agentic systems can create measurable operational value when reasoning, tool use, evaluation, and production integration are designed as parts of the same system.
Responsabilities and Scope
I was responsible for the agent architecture, tool integration, workflow design, deployment, and operational validation.
Internal tools, prompts, runbooks, platform topology, and other confidential implementation details are intentionally excluded from this public case study.