Project Description
Agentic workflow automation uses AI agents to manage complex financial production tasks with minimal human input. Unlike traditional rule-based systems, these agents reason, plan, and dynamically adapt, making them ideal for volatile environments such as trading systems, FIX engines, and post-trade operations.
In financial contexts, agentic workflows can:
- Monitor and triage FIX message flows
- Automate OMS/EMS alert responses
- Classify and escalate trade breaks
- Coordinate data integration and reconciliation tasks across back-office systems
These systems integrate with databases, APIs, and messaging layers to reduce latency, improve decision-making, and scale operational support in real time.
Area of Interest
- Pydantic-AI: A high-performance, schema-driven data validation and parsing tool optimized for AI agents and financial data pipelines. Ideal for structuring FIX messages and validating incoming trading data in real-time.
- LangChain: A framework for building context-aware agents that chain together LLM prompts, memory, tools, and data sources. Perfect for managing dynamic workflows such as trade alert classification and post-trade reporting.
- PyTorch: A flexible and scalable deep learning library used for developing custom LLMs and anomaly detection models. Enables advanced modeling for market pattern recognition and post-trade analytics.
Progress Tracker
Current development and research activity across technologies:
Additional Resources
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