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Advanced Quant & AI EngineeringEnrollment opening soon

AI Research Agent Architecture

Architect AI research agents for finance: tool use, memory, planning, grounded retrieval, evaluation, guardrails and production deployment with full audit logs.

  • Duration10 weeks
  • LevelAdvanced
  • Modules10
  • Lessons40
Enroll nowView curriculum
AI Research Agent Architecture
Proposed fee$1,699USD · 10 weeks

Course overview

AI agents can speed up research work such as reviewing filings, analyzing datasets and drafting reports, but only when they are designed with clear tools, grounded sources and strong controls. This course covers agent architecture end to end: tool interfaces, memory, planning and multi-agent orchestration, retrieval with citations, evaluation and security. You will build and evaluate agents that assist human researchers and record every action they take.

  • Agent tool use
  • Memory and context
  • Planning and orchestration
  • Grounded retrieval
  • Agent evaluation
  • Guardrails and governance

What you will learn

  • Design safe tool interfaces and sandboxed code execution for research agents
  • Manage context and memory across long, multi-step research tasks
  • Compare single-agent and multi-agent designs and add human review checkpoints
  • Ground agent outputs in point-in-time sources with verifiable citations
  • Evaluate agents for faithfulness, tool-use correctness, cost and latency

Curriculum

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

  • What an agent is and is not
  • Tool use and function calling
  • Agent loops and stopping conditions
  • Where agents fit in research workflows

  • Designing safe tool interfaces
  • Data access tools for historical datasets
  • Code execution sandboxes
  • Validating tool results

  • Context window management
  • Short-term and long-term memory
  • Retrieval over research notes
  • State persistence

  • Task decomposition
  • Single-agent and multi-agent designs
  • Supervisor and worker patterns
  • Human-in-the-loop checkpoints

  • Document pipelines for filings and news
  • Hybrid search and reranking
  • Grounding and citation
  • Freshness and point-in-time retrieval

  • Filing and literature review agents
  • Data analysis agents
  • Backtest analysis assistants
  • Report generation with sources

  • Task-based evaluation suites
  • Trajectory and tool-call evaluation
  • Faithfulness and citation checks
  • Regression testing agents

  • Prompt injection and data exfiltration risks
  • Permissions and least privilege
  • Output policies and disclaimers
  • Audit logs for agent actions

  • Serving agents behind APIs
  • Queues, timeouts and retries
  • Cost and latency budgets
  • Observability and tracing

  • Capstone design document
  • Implementation and integration
  • Evaluation report
  • Final demo and review

Practical projects

01

Build a filing review agent that extracts, summarizes and cites sections of company reports

02

Create a data analysis agent that runs sandboxed Python on historical datasets and logs every tool call

03

Design an evaluation suite that measures agent faithfulness, tool-use correctness and cost per task

Skills you will practice

  • LLM APIs
  • Function calling
  • Vector search
  • Python
  • Agent evaluation
  • Tracing

Requirements

Who it's for

  • AI engineers building research tools for financial teams
  • Quant developers adding agents to research workflows
  • Engineers who completed AI Agent Engineering for Financial Research

Prerequisites

  • AI Agent Engineering for Financial Research or equivalent LLM application experience
  • Strong Python skills and familiarity with REST APIs

Learning format

  • Self-paced lessons with architecture patterns and code walkthroughs
  • Agent-building labs with evaluation checkpoints
  • Assignments with code and design reviews
  • Capstone agent system with an 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 architecture patterns and code walkthroughs Agent-building labs with evaluation checkpoints Assignments with code and design reviews

AI Agent Engineering for Financial Research or equivalent LLM application experience. 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 $1,699 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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