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Artificial Intelligence & Machine LearningEnrollment opening soon

AI Agent Engineering for Financial Research

Engineer LLM-based research agents that retrieve documents, call analytical tools and produce traceable financial research outputs with evaluation and guardrails built in.

  • Duration10 weeks
  • LevelAdvanced
  • Modules10
  • Lessons40
Enroll nowView curriculum
AI Agent Engineering for Financial Research
Proposed fee$1,499USD · 10 weeks

Course overview

This course covers the engineering of AI agents that support financial research workflows, such as summarizing filings, querying datasets and running analytical tools. You will design retrieval pipelines, tool interfaces and multi-step agent loops, then build evaluation harnesses, guardrails and observability so outputs remain traceable and reviewable. The emphasis is on reliability, citation of sources and keeping a human researcher in control of every conclusion.

  • LLM fundamentals
  • Retrieval-augmented generation
  • Tool calling and function design
  • Agent orchestration
  • Evaluation harnesses
  • Guardrails and observability

What you will learn

  • Design retrieval pipelines over filings, transcripts and research notes with source citations
  • Implement tool interfaces that let an agent query databases and run Python analytics safely
  • Build multi-step agent loops with planning, memory and error recovery
  • Create evaluation suites that measure faithfulness, tool-use correctness and regressions
  • Deploy an agent service with logging, cost controls and human review checkpoints

Curriculum

10 modules · 40 lessons · 10 weeks. Curriculum preview; the final syllabus may change before enrollment opens.

  • How large language models work
  • Prompting fundamentals
  • Strengths, limits and hallucination
  • Where agents fit in research

  • Parsing filings and transcripts
  • Chunking strategies and metadata
  • Handling tables and numbers
  • Versioning document stores

  • Embeddings and vector search
  • Hybrid keyword and semantic retrieval
  • Reranking and context assembly
  • Citing sources in answers

  • Defining tool schemas
  • SQL query tools with safety limits
  • Sandboxed Python analytics tools
  • Validating tool inputs and outputs

  • Plan, act and observe cycles
  • Short-term and long-term memory
  • Error handling and retries
  • Multi-agent patterns and tradeoffs

  • Schema-constrained generation
  • Generating research summaries
  • Numerical consistency checks
  • Formatting outputs for review

  • Golden datasets and rubrics
  • Faithfulness and grounding metrics
  • Tool-use correctness tests
  • Regression testing across releases

  • Input and output filtering
  • Preventing unsupported claims
  • Access control for data and tools
  • Human-in-the-loop checkpoints

  • Serving agents with FastAPI
  • Tracing, logging and cost tracking
  • Caching and latency control
  • Monitoring quality in production

  • Scoping the research use case
  • Building and integrating components
  • Running the evaluation suite
  • Final review and documentation

Practical projects

01

Build a filing research assistant that answers questions with cited passages from source documents

02

Develop an analytics agent that calls SQL and Python tools to compute metrics and explain its steps

03

Create an evaluation harness with golden question sets and regression tracking for agent releases

Skills you will practice

  • LLM APIs
  • Vector search
  • Tool calling
  • FastAPI
  • PostgreSQL
  • Evaluation design

Requirements

Who it's for

  • Software engineers building AI features for research platforms
  • Quant and research developers automating document-heavy workflows
  • Technical leads evaluating how to use LLM agents responsibly

Prerequisites

  • NLP & Financial News Intelligence or comparable experience with language models
  • Strong Python skills and familiarity with REST APIs, such as from Financial APIs & Data Engineering
  • Basic SQL and database knowledge

Learning format

  • Self-paced lessons with weekly engineering labs
  • Architecture and code reviews on submitted work
  • Live design sessions in cohort runs
  • Capstone agent project with a documented evaluation report

Quantzivo certificates are completion credentials issued by Quantzivo, not accredited qualifications. Courses teach technical and research skills; they do not promise investment returns or provide investment advice.

Frequently asked questions

Self-paced lessons with weekly engineering labs Architecture and code reviews on submitted work Live design sessions in cohort runs

NLP & Financial News Intelligence or comparable experience with language models. See the full list under Requirements.

Yes. Completing the lessons and assessed projects earns a Quantzivo certificate of completion. It is a completion credential issued by Quantzivo, not an accredited qualification.

The $1,499 USD fee is a proposed launch price. Final fees, schedules, refund terms and any regional options are confirmed before any payment is taken.

Quantzivo teaches technical, data and research skills. It does not provide investment advice, trading signals or any promise of returns. Trading involves risk, including loss of capital.
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