Automated Back-Office Task Orchestration for Banking Teams

Added Nov 2025 3 design docs

A great deal of banking work is routine but unavoidable: reconciling transactions, assembling compliance summaries, and drafting customer communications consume analyst hours every day. Orchestrating such tasks so that software executes the steps and documents the results is where agent-based AI meets immediate operational value. The intern builds a modular orchestration system in python. Users define a set of routine banking tasks, and the system runs each one through an automated workflow: langgraph defines the task graphs, an agentic-ai layer sequences and coordinates the steps, and generative AI through openai models produces the human-readable outputs, including reconciliation summaries, compliance digests, and customer communication drafts. Every task run also emits documentation of what was done at each step, and the final deliverables are structured data artifacts plus text-based reports. The architecture is deliberately modular so new task types can be added without rewriting the core. This is a demanding build that teaches workflow automation at a professional standard: decomposing operational processes, orchestrating multi-step agent execution, and generating outputs that are auditable, giving the intern a strong story about applied agentic AI in banking.

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