Presentation Agenda
What is Agentic AI?
Agentic AI refers to autonomous systems powered by LLMs that reason, plan, and act using tools, memory, and dynamic goal pursuit.
Key Characteristics
- • Goes beyond static prompts and responses
- • Combines language understanding with real-world action
- • Structured via planning loops, workflows, and state memory
Why It Matters in Quant Finance
- • Accelerates strategy research and validation
- • Automates reporting, compliance, risk monitoring
- • Handles multi-step workflows (ETL, model training, reporting)
- • Supports dynamic data and market adaptation
Core Capabilities of Agentic Systems
Planning
Break goals into sub-tasks
Tool Use
Invoke APIs, code, SQL, models
Memory
Retain and reuse prior outputs
Reflection
Evaluate past performance
Collaboration
Coordinate with other agents
Agentic Frameworks Landscape
LangChain
Connects LLMs to external tools, APIs, and data sources with structured prompt chains.
- • SQL + API access via chains
- • Memory-enabled agents
- • Function-calling for execution
LangGraph
Builds DAG-like workflows using agents as nodes with shared state and controlled flow.
- • Great for multi-agent, multi-modal workflows
- • Used in portfolio optimization & report generation
- • State sharing + deterministic execution
AutoGen
Multi-agent orchestration via conversation loops. Agents can play different roles.
- • Flexible and research-oriented
- • Ideal for brainstorming, iterative modeling
- • Can build agent debates around model outputs
Google ADK
Offers task agents, tool agents, and parallel runners using workflows like ReAct and Tree-of-Thought.
- • Define agents declaratively
- • Connect to secure tools via extensions
- • Track all agent interactions & state
Other Notable Frameworks
SmolAgents
Minimalist, code-first framework for fast strategy prototyping
CrewAI
Team-based agent workflows with role-based logic
OpenHands
Developer agent platform for autonomous codebase evolution
Cursor
AI coding assistant for quant model development
MCP
Standard interface between models, tools, and agents
A2A
Google's protocol for agent-to-agent communication
Applications in Quant Finance
Agentic AI can augment every part of the quant workflow — from strategy development to execution and risk oversight.
Research & Strategy
-
Factor Research & Alpha Discovery
-
Time-Series Forecasting
-
Derivatives Pricing
-
Trading Strategy Design
Risk & Portfolio
-
Risk Modeling – VaR & Stress Testing
-
Scenario Simulation with Tree-of-Thoughts
-
Portfolio Optimization
-
Report Generation
Data & Operations
-
Research Assistant Agents
-
Sentiment + News Integration
-
Data ETL & Cleaning Agents
-
Natural Language Query Agents
Compliance & Governance
-
Model Risk & Compliance Agents
-
AI Governance in Finance
Integration in Quant Systems
Agentic AI does not operate in a vacuum — it connects with real-world data, platforms, and modeling tools.
Data Platforms
Snowflake Integration
Use LangChain SQL agents or Snowpark containers for secure, query-optimized compute
Bloomberg/FactSet APIs
Agents can use Bloomberg Python API toolkit (blpapi) for live financial data
Weaviate & Vector Search
Use embeddings to store research, filings, reports with semantic RAG-based queries
Modeling Tools
MATLAB + Python Interop
Agents invoke MATLAB pricing/risk models from Python using MCP or MATLAB Engine API
SmolAgents for Execution
Low-latency agents for trade execution logic with feedback loops
Agent Interoperability via A2A
Google's A2A spec enables agent-to-agent messaging for decentralized research
Governance & Control
Logging & Auditability
Store agent action history with timestamps and justification metadata
Governance Frameworks
Integrate with Model Risk Management (SR 11-7, ISO, PRA)
Testing & Validation
Automated test harnesses for LLM outputs and edge case simulation
Case Studies Overview
AI Quant Researcher Agent
Agents explore SSRN/arXiv/Google Scholar using LangChain + Pinecone for vector RAG to summarize findings and generate prototype code.
Key Components
- • Research retrieval and summarization
- • Hypothesis extraction
- • Backtest wrapper generation
Portfolio Manager Agent
LangGraph defines rebalancing workflow with nodes for forecast, optimizer, and compliance check using ADK for secure API access.
Key Components
- • Multi-agent coordination
- • Secure data access
- • Compliance integration
Risk Monitoring Agent
Combines scenario simulation (ToT) + VaR calc using LangChain to explain risk changes in plain English with Slack alert integration.
Key Components
- • Multi-modal risk assessment
- • Natural language explanations
- • Alerting workflows
Automated Report Generator
Uses RAG to fetch portfolio metrics with LangGraph composing attribution story + visuals for PDF, slides, or email exports.
Key Components
- • Data aggregation
- • Narrative generation
- • Multi-format output
Best Practices
Implementation Patterns
-
Start with narrow, well-defined use cases before expanding scope
-
Use LangGraph/ADK for workflow orchestration
-
Implement robust logging with MCP standards
-
Structure agent roles clearly (decision-maker vs observer)
Risk Controls
-
Implement agent approval thresholds
-
Log critical decisions for later review
-
Establish escalation protocols for model deviations
-
Inject human-in-the-loop when confidence low
Testing & Validation
Automated Test Harnesses
For LLM outputs and agent decisions
Edge Case Simulation
Adversarial prompts and stress scenarios
Reproducibility Tests
PaperBench-like benchmarking
Governance Frameworks
Model Risk Management
SR 11-7, ISO, PRA compliance
Agent Roles
Clear separation of duties
Emerging Standards
MCP, A2A, compliance protocols
Future Directions
Challenges
Hallucination & Uncertainty
In LLM outputs requiring robust validation
Autonomy vs Oversight
Finding the right balance for financial applications
Opportunities
Rapid Model Iteration
Accelerating strategy discovery and risk assessment
Embedded Agents
In trading + governance platforms
Emerging Trends
Autonomous Research Labs
AI agents coordinating literature review and validation
Agent Specialization
Domain-specific agents for different financial functions
Regulatory Adaptation
Alignment with ISO, NIST, PRA standards
Q&A + References
Framework References
Let's Discuss
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QuantUniversity
Innovating at the intersection of AI and Quantitative Finance
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