Agentic Job Automation Platform
A graph-based multi-agent system that runs job applications end to end, with resumable, inspectable state between steps.
Python / LangGraph / LLM APIs / Tool Calling
Problem
Job applications are a long chain of small, stateful tasks that break the moment a single-prompt assistant loses context.
Approach
Graph-based multi-agent orchestration with tool calling and structured memory, persisting state between agent steps so a run can be resumed and inspected.
Trade-off
Chose an explicit state graph over a single autonomous agent loop: more wiring and more code per capability, in exchange for runs that can be replayed and debugged step by step.
What broke / what I'd change
Long runs drifted when a tool returned an unexpected shape. Next pass: schema-validate every tool result at the graph edge and fail the node instead of letting the model improvise around it.
Result
End-to-end task execution instead of one-shot suggestions.